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Bang, Chang Seok, Hyun Lim, Hae Min Jeong, and Sung Hyeon Hwang. "Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study." Journal of Medical Internet Research 23, no. 4 (April 15, 2021): e25167. http://dx.doi.org/10.2196/25167.
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Background In a previous study, we examined the use of deep learning models to classify the invasion depth (mucosa-confined versus submucosa-invaded) of gastric neoplasms using endoscopic images. The external test accuracy reached 77.3%. However, model establishment is labor intense, requiring high performance. Automated deep learning (AutoDL) models, which enable fast searching of optimal neural architectures and hyperparameters without complex coding, have been developed. Objective The objective of this study was to establish AutoDL models to classify the invasion depth of gastric neoplasms. Additionally, endoscopist–artificial intelligence interactions were explored. Methods The same 2899 endoscopic images that were employed to establish the previous model were used. A prospective multicenter validation using 206 and 1597 novel images was conducted. The primary outcome was external test accuracy. Neuro-T, Create ML Image Classifier, and AutoML Vision were used in establishing the models. Three doctors with different levels of endoscopy expertise were asked to classify the invasion depth of gastric neoplasms for each image without AutoDL support, with faulty AutoDL support, and with best performance AutoDL support in sequence. Results The Neuro-T–based model reached 89.3% (95% CI 85.1%-93.5%) external test accuracy. For the model establishment time, Create ML Image Classifier showed the fastest time of 13 minutes while reaching 82.0% (95% CI 76.8%-87.2%) external test accuracy. While the expert endoscopist's decisions were not influenced by AutoDL, the faulty AutoDL misled the endoscopy trainee and the general physician. However, this was corrected by the support of the best performance AutoDL model. The trainee gained the most benefit from the AutoDL support. Conclusions AutoDL is deemed useful for the on-site establishment of customized deep learning models. An inexperienced endoscopist with at least a certain level of expertise can benefit from AutoDL support.
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Chen, Yi-Wei, Qingquan Song, and Xia Hu. "Techniques for Automated Machine Learning." ACM SIGKDD Explorations Newsletter 22, no. 2 (January 17, 2021): 35–50. http://dx.doi.org/10.1145/3447556.3447567.
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Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a problem description, its task type, and datasets. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper, we portray AutoML as a bi-level optimization problem, where one problem is nested within another to search the optimum in the search space, and review the current developments of AutoML in terms of three categories, automated feature engineering (AutoFE), automated model and hyperparameter tuning (AutoMHT), and automated deep learning (AutoDL). Stateof- the-art techniques in the three categories are presented. The iterative solver is proposed to generalize AutoML techniques. We summarize popular AutoML frameworks and conclude with current open challenges of AutoML.
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Tuggener, Lukas, Mohammadreza Amirian, Fernando Benites, Pius von Däniken, Prakhar Gupta, Frank-Peter Schilling, and Thilo Stadelmann. "Design Patterns for Resource-Constrained Automated Deep-Learning Methods." AI 1, no. 4 (November 6, 2020): 510–38. http://dx.doi.org/10.3390/ai1040031.
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We present an extensive evaluation of a wide variety of promising design patterns for automated deep-learning (AutoDL) methods, organized according to the problem categories of the 2019 AutoDL challenges, which set the task of optimizing both model accuracy and search efficiency under tight time and computing constraints. We propose structured empirical evaluations as the most promising avenue to obtain design principles for deep-learning systems due to the absence of strong theoretical support. From these evaluations, we distill relevant patterns which give rise to neural network design recommendations. In particular, we establish (a) that very wide fully connected layers learn meaningful features faster; we illustrate (b) how the lack of pretraining in audio processing can be compensated by architecture search; we show (c) that in text processing deep-learning-based methods only pull ahead of traditional methods for short text lengths with less than a thousand characters under tight resource limitations; and lastly we present (d) evidence that in very data- and computing-constrained settings, hyperparameter tuning of more traditional machine-learning methods outperforms deep-learning systems.
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Zimmer, Lucas, Marius Lindauer, and Frank Hutter. "Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 9 (September 1, 2021): 3079–90. http://dx.doi.org/10.1109/tpami.2021.3067763.
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Liu, Zhengying, Adrien Pavao, Zhen Xu, Sergio Escalera, Fabio Ferreira, Isabelle Guyon, Sirui Hong, et al. "Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 9 (September 1, 2021): 3108–25. http://dx.doi.org/10.1109/tpami.2021.3075372.
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Chen, Xu, and Brett Wujek. "AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3537–44. http://dx.doi.org/10.1609/aaai.v34i04.5759.
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Automated machine learning (AutoML) strives to establish an appropriate machine learning model for any dataset automatically with minimal human intervention. Although extensive research has been conducted on AutoML, most of it has focused on supervised learning. Research of automated semi-supervised learning and active learning algorithms is still limited. Implementation becomes more challenging when the algorithm is designed for a distributed computing environment. With this as motivation, we propose a novel automated learning system for distributed active learning (AutoDAL) to address these challenges. First, automated graph-based semi-supervised learning is conducted by aggregating the proposed cost functions from different compute nodes in a distributed manner. Subsequently, automated active learning is addressed by jointly optimizing hyperparameters in both the classification and query selection stages leveraging the graph loss minimization and entropy regularization. Moreover, we propose an efficient distributed active learning algorithm which is scalable for big data by first partitioning the unlabeled data and replicating the labeled data to different worker nodes in the classification stage, and then aggregating the data in the controller in the query selection stage. The proposed AutoDAL algorithm is applied to multiple benchmark datasets and a real-world electrocardiogram (ECG) dataset for classification. We demonstrate that the proposed AutoDAL algorithm is capable of achieving significantly better performance compared to several state-of-the-art AutoML approaches and active learning algorithms.
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Parker-Holder, Jack, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, et al. "Automated Reinforcement Learning (AutoRL): A Survey and Open Problems." Journal of Artificial Intelligence Research 74 (June 1, 2022): 517–68. http://dx.doi.org/10.1613/jair.1.13596.
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The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents. However, the success of RL agents is often highly sensitive to design choices in the training process, which may require tedious and error-prone manual tuning. This makes it challenging to use RL for new problems and also limits its full potential. In many other areas of machine learning, AutoML has shown that it is possible to automate such design choices, and AutoML has also yielded promising initial results when applied to RL. However, Automated Reinforcement Learning (AutoRL) involves not only standard applications of AutoML but also includes additional challenges unique to RL, that naturally produce a different set of methods. As such, AutoRL has been emerging as an important area of research in RL, providing promise in a variety of applications from RNA design to playing games, such as Go. Given the diversity of methods and environments considered in RL, much of the research has been conducted in distinct subfields, ranging from meta-learning to evolution. In this survey, we seek to unify the field of AutoRL, provide a common taxonomy, discuss each area in detail and pose open problems of interest to researchers going forward.
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Cao, Longbing. "Beyond AutoML: Mindful and Actionable AI and AutoAI With Mind and Action." IEEE Intelligent Systems 37, no. 5 (September 1, 2022): 6–18. http://dx.doi.org/10.1109/mis.2022.3207860.
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Lan, Hai, Yuanjia Zhang, Zhifeng Bao, Yu Dong, Dongxu Huang, Liu Tang, and Jian Zhang. "AutoDI." Proceedings of the VLDB Endowment 15, no. 12 (August 2022): 3626–29. http://dx.doi.org/10.14778/3554821.3554860.
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Manual analysis on plan regression is both labor-intensive and inefficient for a large query plan and numerous queries. In this paper, we demonstrate AutoDI, an automatic detection and inference tool that has been developed to investigate why a sub-optimal plan is obtained by analyzing two different plans of the same query. AutoDI consists of two main modules, Difference Finder and Inference. The former aims to find where the two plans are different, and the latter tries to obtain the reasons why the differences come out. In our demonstration, we use a real plan regression in TiDB to show how AutoDI works.
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Yakovlev, Anatoly, Hesam Fathi Moghadam, Ali Moharrer, Jingxiao Cai, Nikan Chavoshi, Venkatanathan Varadarajan, Sandeep R. Agrawal, et al. "Oracle AutoML." Proceedings of the VLDB Endowment 13, no. 12 (August 2020): 3166–80. http://dx.doi.org/10.14778/3415478.3415542.
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Thongprayoon, Charat, Pattharawin Pattharanitima, Andrea G. Kattah, Michael A. Mao, Mira T. Keddis, John J. Dillon, Wisit Kaewput, et al. "Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury." Journal of Clinical Medicine 11, no. 21 (October 24, 2022): 6264. http://dx.doi.org/10.3390/jcm11216264.
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Background: We aimed to develop and validate an automated machine learning (autoML) prediction model for cardiac surgery-associated acute kidney injury (CSA-AKI). Methods: Using 69 preoperative variables, we developed several models to predict post-operative AKI in adult patients undergoing cardiac surgery. Models included autoML and non-autoML types, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN), as well as a logistic regression prediction model. We then compared model performance using area under the receiver operating characteristic curve (AUROC) and assessed model calibration using Brier score on the independent testing dataset. Results: The incidence of CSA-AKI was 36%. Stacked ensemble autoML had the highest predictive performance among autoML models, and was chosen for comparison with other non-autoML and multivariable logistic regression models. The autoML had the highest AUROC (0.79), followed by RF (0.78), XGBoost (0.77), multivariable logistic regression (0.77), ANN (0.75), and DT (0.64). The autoML had comparable AUROC with RF and outperformed the other models. The autoML was well-calibrated. The Brier score for autoML, RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.18, 0.18, 0.21, 0.19, 0.19, and 0.18, respectively. We applied SHAP and LIME algorithms to our autoML prediction model to extract an explanation of the variables that drive patient-specific predictions of CSA-AKI. Conclusion: We were able to present a preoperative autoML prediction model for CSA-AKI that provided high predictive performance that was comparable to RF and superior to other ML and multivariable logistic regression models. The novel approaches of the proposed explainable preoperative autoML prediction model for CSA-AKI may guide clinicians in advancing individualized medicine plans for patients under cardiac surgery.
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Mustafa, Akram, and Mostafa Rahimi Azghadi. "Automated Machine Learning for Healthcare and Clinical Notes Analysis." Computers 10, no. 2 (February 22, 2021): 24. http://dx.doi.org/10.3390/computers10020024.
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Machine learning (ML) has been slowly entering every aspect of our lives and its positive impact has been astonishing. To accelerate embedding ML in more applications and incorporating it in real-world scenarios, automated machine learning (AutoML) is emerging. The main purpose of AutoML is to provide seamless integration of ML in various industries, which will facilitate better outcomes in everyday tasks. In healthcare, AutoML has been already applied to easier settings with structured data such as tabular lab data. However, there is still a need for applying AutoML for interpreting medical text, which is being generated at a tremendous rate. For this to happen, a promising method is AutoML for clinical notes analysis, which is an unexplored research area representing a gap in ML research. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML for clinical notes. To that end, we first introduce the AutoML technology and review its various tools and techniques. We then survey the literature of AutoML in the healthcare industry and discuss the developments specific to clinical settings, as well as those using general AutoML tools for healthcare applications. With this background, we then discuss challenges of working with clinical notes and highlight the benefits of developing AutoML for medical notes processing. Next, we survey relevant ML research for clinical notes and analyze the literature and the field of AutoML in the healthcare industry. Furthermore, we propose future research directions and shed light on the challenges and opportunities this emerging field holds. With this, we aim to assist the community with the implementation of an AutoML platform for medical notes, which if realized can revolutionize patient outcomes.
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Zöller, Marc-André, and Marco F. Huber. "Benchmark and Survey of Automated Machine Learning Frameworks." Journal of Artificial Intelligence Research 70 (January 27, 2021): 409–72. http://dx.doi.org/10.1613/jair.1.11854.
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Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics and machine learning. This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets. Driven by the selected frameworks for evaluation, we summarize and review important AutoML techniques and methods concerning every step in building an ML pipeline. The selected AutoML frameworks are evaluated on 137 data sets from established AutoML benchmark suites.
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KADIOGLU, Muhammet Ali. "End-to-End AutoML Implementation Framework." Eurasia Proceedings of Science Technology Engineering and Mathematics 19 (December 14, 2022): 35–40. http://dx.doi.org/10.55549/epstem.1218713.
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Automated machine learning (AutoML) has been an active research area in recent years. Researchers investigate the potential of AutoML as more stakeholders want to maximize the value of their data. The methods are designed to increase the effectiveness of machine learning (ML), accelerate model development processes, and make it accessible for domain experts that are not ML professionals. The systems without the aid of humans are feasible with AutoML, an area that has been increasingly studied recently. Even though efficiency and automation are two of AutoML's key points, a number of critical steps still require human involvement, such as understanding the characteristics of domain-specific data, defining prediction problems, creating a suitable training dataset, and choosing a promising ML technique. A comprehensive and updated analysis of the state-of-the-art in AutoML is presented in the study. AutoML techniques, including hyperparameter optimization (HPO), feature engineering, and data preparation are presented. As-is prediction structure and AutoML-based benchmark model are compared to show how to implement these methods. It is stated what a real end-to-end machine learning pipeline looks like and which parts of the pipeline have already been automated. Our AutoML implementation framework has been introduced and presented as a road map for the entire ML pipeline. Several unresolved issues with the current AutoML techniques are discussed. The obstacles have been outlined that must be overcome in order to achieve this objective.
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Lazebnik, Teddy, Amit Somech, and Abraham Itzhak Weinberg. "SubStrat." Proceedings of the VLDB Endowment 16, no. 4 (December 2022): 772–80. http://dx.doi.org/10.14778/3574245.3574261.
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Automated machine learning (AutoML) frameworks have become important tools in the data scientist's arsenal, as they dramatically reduce the manual work devoted to the construction of ML pipelines. Such frameworks intelligently search among millions of possible ML pipelines - typically containing feature engineering, model selection, and hyper parameters tuning steps - and finally output an optimal pipeline in terms of predictive accuracy. However, when the dataset is large, each individual configuration takes longer to execute, therefore the overall AutoML running times become increasingly high. To this end, we present SubStrat, an AutoML optimization strategy that tackles the data size, rather than configuration space. It wraps existing AutoML tools, and instead of executing them directly on the entire dataset, SubStrat uses a genetic-based algorithm to find a small yet representative data subset that preserves a particular characteristic of the full data. It then employs the AutoML tool on the small subset, and finally, it refines the resulting pipeline by executing a restricted, much shorter, AutoML process on the large dataset. Our experimental results, performed on three popular AutoML frameworks, Auto-Sklearn, TPOT, and H2O show that SubStrat reduces their running times by 76.3% (on average), with only a 4.15% average decrease in the accuracy of the resulting ML pipeline.
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Liu, Sijia, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn, and Alexander Gray. "An ADMM Based Framework for AutoML Pipeline Configuration." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4892–99. http://dx.doi.org/10.1609/aaai.v34i04.5926.
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We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines. This black-box (gradient-free) optimization with mixed integer & continuous variables is a challenging problem. We propose a novel AutoML scheme by leveraging the alternating direction method of multipliers (ADMM). The proposed framework is able to (i) decompose the optimization problem into easier sub-problems that have a reduced number of variables and circumvent the challenge of mixed variable categories, and (ii) incorporate black-box constraints alongside the black-box optimization objective. We empirically evaluate the flexibility (in utilizing existing AutoML techniques), effectiveness (against open source AutoML toolkits), and unique capability (of executing AutoML with practically motivated black-box constraints) of our proposed scheme on a collection of binary classification data sets from UCI ML & OpenML repositories. We observe that on an average our framework provides significant gains in comparison to other AutoML frameworks (Auto-sklearn & TPOT), highlighting the practical advantages of this framework.
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Li, Yang, Yu Shen, Wentao Zhang, Jiawei Jiang, Bolin Ding, Yaliang Li, Jingren Zhou, et al. "VolcanoML." Proceedings of the VLDB Endowment 14, no. 11 (July 2021): 2167–76. http://dx.doi.org/10.14778/3476249.3476270.
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End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning. Existing AutoML systems, however, suffer from scalability issues when applying to application domains with large, high-dimensional search spaces. We present VOLCANOML, a scalable and extensible framework that facilitates systematic exploration of large AutoML search spaces. VOLCANOML introduces and implements basic building blocks that decompose a large search space into smaller ones, and allows users to utilize these building blocks to compose an execution plan for the AutoML problem at hand. VOLCANOML further supports a Volcano-style execution model - akin to the one supported by modern database systems - to execute the plan constructed. Our evaluation demonstrates that, not only does VOLCANOML raise the level of expressiveness for search space decomposition in AutoML, it also leads to actual findings of decomposition strategies that are significantly more efficient than the ones employed by state-of-the-art AutoML systems such as auto-sklearn.
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Helali, Mossad, Essam Mansour, Ibrahim Abdelaziz, Julian Dolby, and Kavitha Srinivas. "A scalable AutoML approach based on graph neural networks." Proceedings of the VLDB Endowment 15, no. 11 (July 2022): 2428–36. http://dx.doi.org/10.14778/3551793.3551804.
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AutoML systems build machine learning models automatically by performing a search over valid data transformations and learners, along with hyper-parameter optimization for each learner. Many AutoML systems use meta-learning to guide search for optimal pipelines. In this work, we present a novel meta-learning system called KGpip which (1) builds a database of datasets and corresponding pipelines by mining thousands of scripts with program analysis, (2) uses dataset embeddings to find similar datasets in the database based on its content instead of metadata-based features, (3) models AutoML pipeline creation as a graph generation problem, to succinctly characterize the diverse pipelines seen for a single dataset. KGpip's meta-learning is a sub-component for AutoML systems. We demonstrate this by integrating KGpip with two AutoML systems. Our comprehensive evaluation using 121 datasets, including those used by the state-of-the-art systems, shows that KGpip significantly outperforms these systems.
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Yu, Chenyan, Yao Li, Minyue Yin, Jingwen Gao, Liting Xi, Jiaxi Lin, Lu Liu, et al. "Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis." Journal of Personalized Medicine 12, no. 11 (November 19, 2022): 1930. http://dx.doi.org/10.3390/jpm12111930.
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Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow University between 2014 and 2020. Participants were divided into training and validation datasets at a ratio of 8.5:1.5. Models were developed on the H2O AutoML platform in the training dataset, and then were evaluated in the validation dataset by area under receiver operating characteristic curves (AUC). The best AutoML model was interpreted by SHapley Additive exPlanation (SHAP) Plot, Partial Dependence Plots (PDP), and Local Interpretable Model Agnostic Explanation (LIME). Results: The model, based on the extreme gradient boosting (XGBoost) algorithm, performed better (AUC 0.888) than the other AutoML models (logistic regression 0.673, gradient boost machine 0.886, random forest 0.866, deep learning 0.830, stacking 0.850), as well as the existing scorings (the model of end-stage liver disease [MELD] score 0.778, MELD-Na score 0.782, and albumin-bilirubin [ALBI] score 0.662). The most key variable in the XGBoost model was high-density lipoprotein cholesterol, followed by creatinine, white blood cell count, international normalized ratio, etc. Conclusion: The AutoML model based on the XGBoost algorithm presented better performance than the existing scoring systems for predicting 30-day mortality in patients with non-cholestatic cirrhosis. It shows the promise of AutoML in its future medical application.
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Koh, Joshua C. O., German Spangenberg, and Surya Kant. "Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping." Remote Sensing 13, no. 5 (February 25, 2021): 858. http://dx.doi.org/10.3390/rs13050858.
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Automated machine learning (AutoML) has been heralded as the next wave in artificial intelligence with its promise to deliver high-performance end-to-end machine learning pipelines with minimal effort from the user. However, despite AutoML showing great promise for computer vision tasks, to the best of our knowledge, no study has used AutoML for image-based plant phenotyping. To address this gap in knowledge, we examined the application of AutoML for image-based plant phenotyping using wheat lodging assessment with unmanned aerial vehicle (UAV) imagery as an example. The performance of an open-source AutoML framework, AutoKeras, in image classification and regression tasks was compared to transfer learning using modern convolutional neural network (CNN) architectures. For image classification, which classified plot images as lodged or non-lodged, transfer learning with Xception and DenseNet-201 achieved the best classification accuracy of 93.2%, whereas AutoKeras had a 92.4% accuracy. For image regression, which predicted lodging scores from plot images, transfer learning with DenseNet-201 had the best performance (R2 = 0.8303, root mean-squared error (RMSE) = 9.55, mean absolute error (MAE) = 7.03, mean absolute percentage error (MAPE) = 12.54%), followed closely by AutoKeras (R2 = 0.8273, RMSE = 10.65, MAE = 8.24, MAPE = 13.87%). In both tasks, AutoKeras models had up to 40-fold faster inference times compared to the pretrained CNNs. AutoML has significant potential to enhance plant phenotyping capabilities applicable in crop breeding and precision agriculture.
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Paldino, Gian Marco, Jacopo De Stefani, Fabrizio De Caro, and Gianluca Bontempi. "Does AutoML Outperform Naive Forecasting?" Engineering Proceedings 5, no. 1 (July 5, 2021): 36. http://dx.doi.org/10.3390/engproc2021005036.
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The availability of massive amounts of temporal data opens new perspectives of knowledge extraction and automated decision making for companies and practitioners. However, learning forecasting models from data requires a knowledgeable data science or machine learning (ML) background and expertise, which is not always available to end-users. This gap fosters a growing demand for frameworks automating the ML pipeline and ensuring broader access to the general public. Automatic machine learning (AutoML) provides solutions to build and validate machine learning pipelines minimizing the user intervention. Most of those pipelines have been validated in static supervised learning settings, while an extensive validation in time series prediction is still missing. This issue is particularly important in the forecasting community, where the relevance of machine learning approaches is still under debate. This paper assesses four existing AutoML frameworks (AutoGluon, H2O, TPOT, Auto-sklearn) on a number of forecasting challenges (univariate and multivariate, single-step and multi-step ahead) by benchmarking them against simple and conventional forecasting strategies (e.g., naive and exponential smoothing). The obtained results highlight that AutoML approaches are not yet mature enough to address generic forecasting tasks once compared with faster yet more basic statistical forecasters. In particular, the tested AutoML configurations, on average, do not significantly outperform a Naive estimator. Those results, yet preliminary, should not be interpreted as a rejection of AutoML solutions in forecasting but as an encouragement to a more rigorous validation of their limits and perspectives.
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Škrlj, Blaž, Matej Bevec, and Nada Lavrač. "Multimodal AutoML via Representation Evolution." Machine Learning and Knowledge Extraction 5, no. 1 (December 23, 2022): 1–13. http://dx.doi.org/10.3390/make5010001.
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With the increasing amounts of available data, learning simultaneously from different types of inputs is becoming necessary to obtain robust and well-performing models. With the advent of representation learning in recent years, lower-dimensional vector-based representations have become available for both images and texts, while automating simultaneous learning from multiple modalities remains a challenging problem. This paper presents an AutoML (automated machine learning) approach to automated machine learning model configuration identification for data composed of two modalities: texts and images. The approach is based on the idea of representation evolution, the process of automatically amplifying heterogeneous representations across several modalities, optimized jointly with a collection of fast, well-regularized linear models. The proposed approach is benchmarked against 11 unimodal and multimodal (texts and images) approaches on four real-life benchmark datasets from different domains. It achieves competitive performance with minimal human effort and low computing requirements, enabling learning from multiple modalities in automated manner for a wider community of researchers.
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Singpai, Bodin, and Desheng Wu. "Using a DEA–AutoML Approach to Track SDG Achievements." Sustainability 12, no. 23 (December 4, 2020): 10124. http://dx.doi.org/10.3390/su122310124.
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Each country needs to monitor progress on their Sustainable Development Goals (SDGs) to develop strategies that meet the expectations of the United Nations. Data envelope analysis (DEA) can help identify best practices for SDGs by setting goals to compete against. Automated machine learning (AutoML) simplifies machine learning for researchers who need less time and manpower to predict future situations. This work introduces an integrative method that integrates DEA and AutoML to assess and predict performance in SDGs. There are two experiments with different data properties in their interval and correlation to demonstrate the approach. Three prediction targets are set to measure performance in the regression, classification, and multi-target regression algorithms. The back-propagation neural network (BPNN) is used to validate the outputs of the AutoML. As a result, AutoML can outperform BPNN for regression and classification prediction problems. Low standard deviation (SD) data result in poor prediction performance for the BPNN, but does not have a significant impact on AutoML. Highly correlated data result in a higher accuracy, but does not significantly affect the R-squared values between the actual and predicted values. This integrative approach can accurately predict the projected outputs, which can be used as national goals to transform an inefficient country into an efficient country.
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Keeling, Stephanie S., Malcolm F. McDonald, Adrish Anand, Cameron R. Goff, Caroline R. Christmann, Spencer C. Barrett, Michael Kueht, John A. Goss, George Cholankeril, and Abbas Rana. "Do Patients with Autoimmune Conditions Have Less Access to Liver Transplantation despite Superior Outcomes?" Journal of Personalized Medicine 12, no. 7 (July 17, 2022): 1159. http://dx.doi.org/10.3390/jpm12071159.
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Orthotopic liver transplantation (OLT) is a lifesaving therapy for patients with irreversible liver damage caused by autoimmune liver diseases (AutoD) including autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and primary sclerosing cholangitis (PSC). Currently, it is unclear how access to transplantation differs among patients with various etiologies of liver disease. Our aim is to evaluate the likelihood of transplant and the long-term patient and graft survival after OLT for each etiology for transplantation from 2000 to 2021. We conducted a large retrospective study of United Network for Organ Sharing (UNOS) liver transplant patients in five 4-year eras with five cohorts: AutoD (PBC, PSC, AIH cirrhosis), alcohol-related liver disease (ALD), hepatocellular carcinoma (HCC), viral hepatitis, and nonalcoholic steatohepatitis (NASH). We conducted a multivariate analysis for probability of transplant. Intent-to-treat (ITT) analysis was performed to assess the 10-year survival differences for each listing diagnosis while accounting for both waitlist and post-transplant survival. Across all eras, autoimmune conditions had a lower adjusted probability of transplant of 0.92 (0.92, 0.93) compared to ALD 0.97 (0.97, 0.97), HCC 1.08 (1.07, 1.08), viral hepatitis 0.99 (0.99, 0.99), and NASH 0.99 (0.99, 1.00). Patients with AutoD had significantly better post-transplant patient and graft survival than ALD, HCC, viral hepatitis, and NASH in each and across all eras (p-values all < 0.001). Patients with AutoD had superior ITT survival (p-value < 0.001, log rank test). In addition, the waitlist survival for patients with AutoD compared to other listing diagnoses was improved with the exception of ALD, which showed no significant difference (p-value = 0.1056, log rank test). Despite a superior 10-year graft and patient survival in patients transplanted for AutoD, patients with AutoD have a significantly lower probability of receiving a liver transplant compared to those transplanted for HCC, ALD, viral hepatitis, and NASH. Patients with AutoD may benefit from improved liver allocation while maintaining superior waitlist and post-transplant survival. Decreased access in spite of appropriate outcomes for patients poses a significant risk for increased morbidity for patients with AutoD.
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Vaccaro, Lorenzo, Giuseppe Sansonetti, and Alessandro Micarelli. "An Empirical Review of Automated Machine Learning." Computers 10, no. 1 (January 13, 2021): 11. http://dx.doi.org/10.3390/computers10010011.
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In recent years, Automated Machine Learning (AutoML) has become increasingly important in Computer Science due to the valuable potential it offers. This is testified by the high number of works published in the academic field and the significant efforts made in the industrial sector. However, some problems still need to be resolved. In this paper, we review some Machine Learning (ML) models and methods proposed in the literature to analyze their strengths and weaknesses. Then, we propose their use—alone or in combination with other approaches—to provide possible valid AutoML solutions. We analyze those solutions from a theoretical point of view and evaluate them empirically on three Atari games from the Arcade Learning Environment. Our goal is to identify what, we believe, could be some promising ways to create truly effective AutoML frameworks, therefore able to replace the human expert as much as possible, thereby making easier the process of applying ML approaches to typical problems of specific domains. We hope that the findings of our study will provide useful insights for future research work in AutoML.
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Alsharef, Ahmad, Sonia ., Karan Kumar, and Celestine Iwendi. "Time Series Data Modeling Using Advanced Machine Learning and AutoML." Sustainability 14, no. 22 (November 17, 2022): 15292. http://dx.doi.org/10.3390/su142215292.
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A prominent area of data analytics is “timeseries modeling” where it is possible to forecast future values for the same variable using previous data. Numerous usage examples, including the economy, the weather, stock prices, and the development of a corporation, demonstrate its significance. Experiments with time series forecasting utilizing machine learning (ML), deep learning (DL), and AutoML are conducted in this paper. Its primary contribution consists of addressing the forecasting problem by experimenting with additional ML and DL models and AutoML frameworks and expanding the AutoML experimental knowledge. In addition, it contributes by breaking down barriers found in past experimental studies in this field by using more sophisticated methods. The datasets this empirical research utilized were secondary quantitative data of the real prices of the currently most used cryptocurrencies. We found that AutoML for timeseries is still in the development stage and necessitates more study to be a viable solution since it was unable to outperform manually designed ML and DL models. The demonstrated approaches may be utilized as a baseline for predicting timeseries data.
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Marinescu, Radu, Akihiro Kishimoto, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, and Adi Botea. "Searching for Machine Learning Pipelines Using a Context-Free Grammar." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 8902–11. http://dx.doi.org/10.1609/aaai.v35i10.17077.
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AutoML automatically selects, composes and parameterizes machine learning algorithms into a workflow or pipeline of operations that aims at maximizing performance on a given dataset. Although current methods for AutoML achieved impressive results they mostly concentrate on optimizing fixed linear workflows. In this paper, we take a different approach and focus on generating and optimizing pipelines of complex directed acyclic graph shapes. These complex pipeline structure may lead to discovering hidden features and thus boost performance considerably. We explore the power of heuristic search and context-free grammars to search and optimize these kinds of pipelines. Experiments on various benchmark datasets show that our approach is highly competitive and often outperforms existing AutoML systems.
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Li, Yu-Feng, Hai Wang, Tong Wei, and Wei-Wei Tu. "Towards Automated Semi-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4237–44. http://dx.doi.org/10.1609/aaai.v33i01.33014237.
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Automated Machine Learning (AutoML) aims to build an appropriate machine learning model for any unseen dataset automatically, i.e., without human intervention. Great efforts have been devoted on AutoML while they typically focus on supervised learning. In many applications, however, semisupervised learning (SSL) are widespread and current AutoML systems could not well address SSL problems. In this paper, we propose to present an automated learning system for SSL (AUTO-SSL). First, meta-learning with enhanced meta-features is employed to quickly suggest some instantiations of the SSL techniques which are likely to perform quite well. Second, a large margin separation method is proposed to fine-tune the hyperparameters and more importantly, alleviate performance deterioration. The basic idea is that, if a certain hyperparameter owns a high quality, its predictive results on unlabeled data may have a large margin separation. Extensive empirical results over 200 cases demonstrate that our proposal on one side achieves highly competitive or better performance compared to the state-of-the-art AutoML system AUTO-SKLEARN and classical SSL techniques, on the other side unlike classical SSL techniques which often significantly degenerate performance, our proposal seldom suffers from such deficiency.
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Shi, M., and Weigang Shen. "Automatic Modeling for Concrete Compressive Strength Prediction Using Auto-Sklearn." Buildings 12, no. 9 (September 7, 2022): 1406. http://dx.doi.org/10.3390/buildings12091406.
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Machine learning is widely used for predicting the compressive strength of concrete. However, the machine learning modeling process relies on expert experience. Automated machine learning (AutoML) aims to automatically select optimal data preprocessing methods, feature preprocessing methods, machine learning algorithms, and hyperparameters according to the datasets used, to obtain high-precision prediction models. However, the effectiveness of modeling concrete compressive strength using AutoML has not been verified. This study attempts to fill the above research gap. We construct a database comprising four different types of concrete datasets and compare one AutoML algorithm (Auto-Sklearn) against five ML algorithms. The results show that Auto-Sklearn can automatically build an accurate concrete compressive strength prediction model without relying on expert experience. In addition, Auto-Sklearn achieves the highest accuracy for all four datasets, with an average R2 of 0.953; the average R2 values of the ML models with tuned hyperparameters range from 0.909 to 0.943. This study verifies for the first time the feasibility of AutoML for concrete compressive strength prediction, to allow concrete engineers to easily build accurate concrete compressive strength prediction models without relying on a large amount of ML modeling experience.
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Garmpis, Spyridon, Manolis Maragoudakis, and Aristogiannis Garmpis. "Assisting Educational Analytics with AutoML Functionalities." Computers 11, no. 6 (June 15, 2022): 97. http://dx.doi.org/10.3390/computers11060097.
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The plethora of changes that have taken place in policy formulations on higher education in recent years in Greece has led to unification, the abolition of departments or technological educational institutions (TEI) and mergers at universities. As a result, many students are required to complete their studies in departments of the abolished TEI. Dropout or a delay in graduation is a significant problem that results from newly joined students at the university, in addition to the provision of studies. There are various reasons for this, with student performance during studies being one of the major contributing factors. This study was aimed at predicting the time required for weak students to pass their courses so as to allow the university to develop strategic programs that will help them improve performance and graduate in time. This paper presents various components of educational data mining incorporating a new state-of-the-art strategy, called AutoML, which is used to find the best models and parameters and is capable of predicting the length of time required for students to pass their courses using their past course performance and academic information. A dataset of 23,687 “Computer Networking” module students was used to train and evaluate the classification of a model developed in the KNIME Analytics (open source) data science platform. The accuracy of the model was measured using well-known evaluation criteria, such as precision, recall, and F-measure. The model was applied to data related to three basic courses and correctly predicted approximately 92% of students’ performance and, specifically, students who are likely to drop out or experience a delay before graduating.
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Bender, Janek, Martin Trat, and Jivka Ovtcharova. "Benchmarking AutoML-Supported Lead Time Prediction." Procedia Computer Science 200 (2022): 482–94. http://dx.doi.org/10.1016/j.procs.2022.01.246.
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Halvari, Tuomas, Jukka K. Nurminen, and Tommi Mikkonen. "Testing the Robustness of AutoML Systems." Electronic Proceedings in Theoretical Computer Science 319 (July 23, 2020): 103–16. http://dx.doi.org/10.4204/eptcs.319.8.
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Weng, Ziqiao. "From Conventional Machine Learning to AutoML." Journal of Physics: Conference Series 1207 (April 2019): 012015. http://dx.doi.org/10.1088/1742-6596/1207/1/012015.
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Dos Santos, Maria Victória Rodrigues, Gabriel Mac'Hamilton Renaux Alves, and Alexandre Magno de Andrade Maciel. "Benchmarking de Sistemas AutoML Open-source." Revista de Engenharia e Pesquisa Aplicada 7, no. 3 (November 29, 2022): 19–28. http://dx.doi.org/10.25286/repa.v7i3.2456.
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Este estudo propõe comparar três sistemas AutoML (Aprendizado de Máquina Automatizado) de código aberto mais conhecidos, o Auto-WEKA, Auto-Sklearn e TPOT, em termos de funcionamento em cada parte do fluxo de um AutoML, e algoritmos suportados em cada parte desse fluxo. O Aprendizado de Máquina Automatizado é uma ferramenta que automatiza o resultado do aprendizado de máquina com o mínimo de esforço humano possível. Este trabalho mostra que, para determinados tipos de dados e objetivos de previsão, o usuário, seja estudante ou profissional da área de ciência de dados, deve se atentar a cada ferramenta, pois implica nos resultados obtidos e as predições podem ficar mais refinadas ou não.
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Angarita-Zapata, Juan S., Gina Maestre-Gongora, and Jenny Fajardo Calderín. "A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities." Sensors 21, no. 24 (December 16, 2021): 8401. http://dx.doi.org/10.3390/s21248401.
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Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellín, Bogotá, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas.
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Li, Kai-Yun, Niall G. Burnside, Raul Sampaio de Lima, Miguel Villoslada Peciña, Karli Sepp, Victor Henrique Cabral Pinheiro, Bruno Rucy Carneiro Alves de Lima, Ming-Der Yang, Ants Vain, and Kalev Sepp. "An Automated Machine Learning Framework in Unmanned Aircraft Systems: New Insights into Agricultural Management Practices Recognition Approaches." Remote Sensing 13, no. 16 (August 12, 2021): 3190. http://dx.doi.org/10.3390/rs13163190.
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The recent trend of automated machine learning (AutoML) has been driving further significant technological innovation in the application of artificial intelligence from its automated algorithm selection and hyperparameter optimization of the deployable pipeline model for unraveling substance problems. However, a current knowledge gap lies in the integration of AutoML technology and unmanned aircraft systems (UAS) within image-based data classification tasks. Therefore, we employed a state-of-the-art (SOTA) and completely open-source AutoML framework, Auto-sklearn, which was constructed based on one of the most widely used ML systems: Scikit-learn. It was combined with two novel AutoML visualization tools to focus particularly on the recognition and adoption of UAS-derived multispectral vegetation indices (VI) data across a diverse range of agricultural management practices (AMP). These include soil tillage methods (STM), cultivation methods (CM), and manure application (MA), and are under the four-crop combination fields (i.e., red clover-grass mixture, spring wheat, pea-oat mixture, and spring barley). Furthermore, they have currently not been efficiently examined and accessible parameters in UAS applications are absent for them. We conducted the comparison of AutoML performance using three other common machine learning classifiers, namely Random Forest (RF), support vector machine (SVM), and artificial neural network (ANN). The results showed AutoML achieved the highest overall classification accuracy numbers after 1200 s of calculation. RF yielded the second-best classification accuracy, and SVM and ANN were revealed to be less capable among some of the given datasets. Regarding the classification of AMPs, the best recognized period for data capture occurred in the crop vegetative growth stage (in May). The results demonstrated that CM yielded the best performance in terms of classification, followed by MA and STM. Our framework presents new insights into plant–environment interactions with capable classification capabilities. It further illustrated the automatic system would become an important tool in furthering the understanding for future sustainable smart farming and field-based crop phenotyping research across a diverse range of agricultural environmental assessment and management applications.
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Karmaker (“Santu”), Shubhra Kanti, Md Mahadi Hassan, Micah J. Smith, Lei Xu, Chengxiang Zhai, and Kalyan Veeramachaneni. "AutoML to Date and Beyond: Challenges and Opportunities." ACM Computing Surveys 54, no. 8 (November 30, 2022): 1–36. http://dx.doi.org/10.1145/3470918.
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As big data becomes ubiquitous across domains, and more and more stakeholders aspire to make the most of their data, demand for machine learning tools has spurred researchers to explore the possibilities of automated machine learning (AutoML). AutoML tools aim to make machine learning accessible for non-machine learning experts (domain experts), to improve the efficiency of machine learning, and to accelerate machine learning research. But although automation and efficiency are among AutoML’s main selling points, the process still requires human involvement at a number of vital steps, including understanding the attributes of domain-specific data, defining prediction problems, creating a suitable training dataset, and selecting a promising machine learning technique. These steps often require a prolonged back-and-forth that makes this process inefficient for domain experts and data scientists alike and keeps so-called AutoML systems from being truly automatic. In this review article, we introduce a new classification system for AutoML systems, using a seven-tiered schematic to distinguish these systems based on their level of autonomy. We begin by describing what an end-to-end machine learning pipeline actually looks like, and which subtasks of the machine learning pipeline have been automated so far. We highlight those subtasks that are still done manually—generally by a data scientist—and explain how this limits domain experts’ access to machine learning. Next, we introduce our novel level-based taxonomy for AutoML systems and define each level according to the scope of automation support provided. Finally, we lay out a roadmap for the future, pinpointing the research required to further automate the end-to-end machine learning pipeline and discussing important challenges that stand in the way of this ambitious goal.
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Liu, Jiabin, Fu Zhu, Chengliang Chai, Yuyu Luo, and Nan Tang. "Automatic data acquisition for deep learning." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 2739–42. http://dx.doi.org/10.14778/3476311.3476333.
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Deep learning (DL) has widespread applications and has revolutionized many industries. Although automated machine learning (AutoML) can help us away from coding for DL models, the acquisition of lots of high-quality data for model training remains a main bottleneck for many DL projects, simply because it requires high human cost. Despite many works on weak supervision ( i.e. , adding weak labels to seen data) and data augmentation ( i.e. , generating more data based on seen data), automatically acquiring training data, via smartly searching a pool of training data collected from open ML benchmarks and data markets, is not explored. In this demonstration, we demonstrate a new system, automatic data acquisition (AutoData), which automatically searches training data from a heterogeneous data repository and interacts with AutoML. It faces two main challenges. (1) How to search high-quality data from a large repository for a given DL task? (2) How does AutoData interact with AutoML to guide the search? To address these challenges, we propose a reinforcement learning (RL)-based framework in AutoData to guide the iterative search process. AutoData encodes current training data and feedbacks of AutoML, learns a policy to search fresh data, and trains in iterations. We demonstrate with two real-life scenarios, image classification and relational data prediction, showing that AutoData can select high-quality data to improve the model.
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Ikemura, Kenji, Eran Bellin, Yukako Yagi, Henny Billett, Mahmoud Saada, Katelyn Simone, Lindsay Stahl, James Szymanski, D. Y. Goldstein, and Morayma Reyes Gil. "Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study." Journal of Medical Internet Research 23, no. 2 (February 26, 2021): e23458. http://dx.doi.org/10.2196/23458.
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Background During a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. Machine learning models have been proposed to accurately predict COVID-19 disease severity. Previous studies have typically tested only one machine learning algorithm and limited performance evaluation to area under the curve analysis. To obtain the best results possible, it may be important to test different machine learning algorithms to find the best prediction model. Objective In this study, we aimed to use automated machine learning (autoML) to train various machine learning algorithms. We selected the model that best predicted patients’ chances of surviving a SARS-CoV-2 infection. In addition, we identified which variables (ie, vital signs, biomarkers, comorbidities, etc) were the most influential in generating an accurate model. Methods Data were retrospectively collected from all patients who tested positive for COVID-19 at our institution between March 1 and July 3, 2020. We collected 48 variables from each patient within 36 hours before or after the index time (ie, real-time polymerase chain reaction positivity). Patients were followed for 30 days or until death. Patients’ data were used to build 20 machine learning models with various algorithms via autoML. The performance of machine learning models was measured by analyzing the area under the precision-recall curve (AUPCR). Subsequently, we established model interpretability via Shapley additive explanation and partial dependence plots to identify and rank variables that drove model predictions. Afterward, we conducted dimensionality reduction to extract the 10 most influential variables. AutoML models were retrained by only using these 10 variables, and the output models were evaluated against the model that used 48 variables. Results Data from 4313 patients were used to develop the models. The best model that was generated by using autoML and 48 variables was the stacked ensemble model (AUPRC=0.807). The two best independent models were the gradient boost machine and extreme gradient boost models, which had an AUPRC of 0.803 and 0.793, respectively. The deep learning model (AUPRC=0.73) was substantially inferior to the other models. The 10 most influential variables for generating high-performing models were systolic and diastolic blood pressure, age, pulse oximetry level, blood urea nitrogen level, lactate dehydrogenase level, D-dimer level, troponin level, respiratory rate, and Charlson comorbidity score. After the autoML models were retrained with these 10 variables, the stacked ensemble model still had the best performance (AUPRC=0.791). Conclusions We used autoML to develop high-performing models that predicted the survival of patients with COVID-19. In addition, we identified important variables that correlated with mortality. This is proof of concept that autoML is an efficient, effective, and informative method for generating machine learning–based clinical decision support tools.
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Kasimati, Aikaterini, Borja Espejo-García, Nicoleta Darra, and Spyros Fountas. "Predicting Grape Sugar Content under Quality Attributes Using Normalized Difference Vegetation Index Data and Automated Machine Learning." Sensors 22, no. 9 (April 23, 2022): 3249. http://dx.doi.org/10.3390/s22093249.
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Wine grapes need frequent monitoring to achieve high yields and quality. Non-destructive methods, such as proximal and remote sensing, are commonly used to estimate crop yield and quality characteristics, and spectral vegetation indices (VIs) are often used to present site-specific information. Analysis of laboratory samples is the most popular method for determining the quality characteristics of grapes, although it is time-consuming and expensive. In recent years, several machine learning-based methods have been developed to predict crop quality. Although these techniques require the extensive involvement of experts, automated machine learning (AutoML) offers the possibility to improve this task, saving time and resources. In this paper, we propose an innovative approach for robust prediction of grape quality attributes by combining open-source AutoML techniques and Normalized Difference Vegetation Index (NDVI) data for vineyards obtained from four different platforms-two proximal vehicle-mounted canopy reflectance sensors, orthomosaics from UAV images and Sentinel-2 remote sensing imagery-during the 2019 and 2020 growing seasons. We investigated AutoML, extending our earlier work on manually fine-tuned machine learning methods. Results of the two approaches using Ordinary Least Square (OLS), Theil-Sen and Huber regression models and tree-based methods were compared. Support Vector Machines (SVMs) and Automatic Relevance Determination (ARD) were included in the analysis and different combinations of sensors and data collected over two growing seasons were investigated. Results showed promising performance of Unmanned Aerial Vehicle (UAV) and Spectrosense+ GPS data in predicting grape sugars, especially in mid to late season with full canopy growth. Regression models with both manually fine-tuned ML (R² = 0.61) and AutoML (R² = 0.65) provided similar results, with the latter slightly improved for both 2019 and 2020. When combining multiple sensors and growth stages per year, the coefficient of determination R² improved even more averaging 0.66 for the best-fitting regressions. Also, when considering combinations of sensors and growth stages across both cropping seasons, UAV and Spectrosense+ GPS, as well as Véraison and Flowering, each had the highest average R² values. These performances are consistent with previous work on machine learning algorithms that were manually fine-tuned. These results suggest that AutoML has greater long-term performance potential. To increase the efficiency of crop quality prediction, a balance must be struck between manual expert work and AutoML.
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Niño-Adan, Iratxe, Itziar Landa-Torres, Diana Manjarres, Eva Portillo, and Lucía Orbe. "Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column." Sensors 21, no. 12 (June 9, 2021): 3991. http://dx.doi.org/10.3390/s21123991.
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Refineries are complex industrial systems that transform crude oil into more valuable subproducts. Due to the advances in sensors, easily measurable variables are continuously monitored and several data-driven soft-sensors are proposed to control the distillation process and the quality of the resultant subproducts. However, data preprocessing and soft-sensor modelling are still complex and time-consuming tasks that are expected to be automatised in the context of Industry 4.0. Although recently several automated learning (autoML) approaches have been proposed, these rely on model configuration and hyper-parameters optimisation. This paper advances the state-of-the-art by proposing an autoML approach that selects, among different normalisation and feature weighting preprocessing techniques and various well-known Machine Learning (ML) algorithms, the best configuration to create a reliable soft-sensor for the problem at hand. As proven in this research, each normalisation method transforms a given dataset differently, which ultimately affects the ML algorithm performance. The presented autoML approach considers the features preprocessing importance, including it, and the algorithm selection and configuration, as a fundamental stage of the methodology. The proposed autoML approach is applied to real data from a refinery in the Basque Country to create a soft-sensor in order to complement the operators’ decision-making that, based on the operational variables of a distillation process, detects 400 min in advance with 98.925% precision if the resultant product does not reach the quality standards.
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Chou, Austin, Abel Torres-Espin, Nikos Kyritsis, J. Russell Huie, Sarah Khatry, Jeremy Funk, Jennifer Hay, et al. "Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome." PLOS ONE 17, no. 4 (April 7, 2022): e0265254. http://dx.doi.org/10.1371/journal.pone.0265254.
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Artificial intelligence and machine learning (AI/ML) is becoming increasingly more accessible to biomedical researchers with significant potential to transform biomedicine through optimization of highly-accurate predictive models and enabling better understanding of disease biology. Automated machine learning (AutoML) in particular is positioned to democratize artificial intelligence (AI) by reducing the amount of human input and ML expertise needed. However, successful translation of AI/ML in biomedicine requires moving beyond optimizing only for prediction accuracy and towards establishing reproducible clinical and biological inferences. This is especially challenging for clinical studies on rare disorders where the smaller patient cohorts and corresponding sample size is an obstacle for reproducible modeling results. Here, we present a model-agnostic framework to reinforce AutoML using strategies and tools of explainable and reproducible AI, including novel metrics to assess model reproducibility. The framework enables clinicians to interpret AutoML-generated models for clinical and biological verifiability and consequently integrate domain expertise during model development. We applied the framework towards spinal cord injury prognostication to optimize the intraoperative hemodynamic range during injury-related surgery and additionally identified a strong detrimental relationship between intraoperative hypertension and patient outcome. Furthermore, our analysis captured how evolving clinical practices such as faster time-to-surgery and blood pressure management affect clinical model development. Altogether, we illustrate how expert-augmented AutoML improves inferential reproducibility for biomedical discovery and can ultimately build trust in AI processes towards effective clinical integration.
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Ma, Junwei, Sheng Jiang, Zhiyang Liu, Zhiyuan Ren, Dongze Lei, Chunhai Tan, and Haixiang Guo. "Machine Learning Models for Slope Stability Classification of Circular Mode Failure: An Updated Database and Automated Machine Learning (AutoML) Approach." Sensors 22, no. 23 (November 25, 2022): 9166. http://dx.doi.org/10.3390/s22239166.
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Slope failures lead to large casualties and catastrophic societal and economic consequences, thus potentially threatening access to sustainable development. Slope stability assessment, offering potential long-term benefits for sustainable development, remains a challenge for the practitioner and researcher. In this study, for the first time, an automated machine learning (AutoML) approach was proposed for model development and slope stability assessments of circular mode failure. An updated database with 627 cases consisting of the unit weight, cohesion, and friction angle of the slope materials; slope angle and height; pore pressure ratio; and corresponding stability status has been established. The stacked ensemble of the best 1000 models was automatically selected as the top model from 8208 trained models using the H2O-AutoML platform, which requires little expert knowledge or manual tuning. The top-performing model outperformed the traditional manually tuned and metaheuristic-optimized models, with an area under the receiver operating characteristic curve (AUC) of 0.970 and accuracy (ACC) of 0.904 based on the testing dataset and achieving a maximum lift of 2.1. The results clearly indicate that AutoML can provide an effective automated solution for machine learning (ML) model development and slope stability classification of circular mode failure based on extensive combinations of algorithm selection and hyperparameter tuning (CASHs), thereby reducing human efforts in model development. The proposed AutoML approach has the potential for short-term severity mitigation of geohazard and achieving long-term sustainable development goals.
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Liu, Denghui, Chi Xu, Wenjun He, Zhimeng Xu, Wenqi Fu, Lei Zhang, Jie Yang, et al. "AutoGenome: An AutoML tool for genomic research." Artificial Intelligence in the Life Sciences 1 (December 2021): 100017. http://dx.doi.org/10.1016/j.ailsci.2021.100017.
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Bruzón, Adrián G., Patricia Arrogante-Funes, Fátima Arrogante-Funes, Fidel Martín-González, Carlos J. Novillo, Rubén R. Fernández, René Vázquez-Jiménez, et al. "Landslide Susceptibility Assessment Using an AutoML Framework." International Journal of Environmental Research and Public Health 18, no. 20 (October 19, 2021): 10971. http://dx.doi.org/10.3390/ijerph182010971.
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The risks associated with landslides are increasing the personal losses and material damages in more and more areas of the world. These natural disasters are related to geological and extreme meteorological phenomena (e.g., earthquakes, hurricanes) occurring in regions that have already suffered similar previous natural catastrophes. Therefore, to effectively mitigate the landslide risks, new methodologies must better identify and understand all these landslide hazards through proper management. Within these methodologies, those based on assessing the landslide susceptibility increase the predictability of the areas where one of these disasters is most likely to occur. In the last years, much research has used machine learning algorithms to assess susceptibility using different sources of information, such as remote sensing data, spatial databases, or geological catalogues. This study presents the first attempt to develop a methodology based on an automatic machine learning (AutoML) framework. These frameworks are intended to facilitate the development of machine learning models, with the aim to enable researchers focus on data analysis. The area to test/validate this study is the center and southern region of Guerrero (Mexico), where we compare the performance of 16 machine learning algorithms. The best result achieved is the extra trees with an area under the curve (AUC) of 0.983. This methodology yields better results than other similar methods because using an AutoML framework allows to focus on the treatment of the data, to better understand input variables and to acquire greater knowledge about the processes involved in the landslides.
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Jiang, Xuetao, Binbin Yong, Soheila Garshasbi, Jun Shen, Meiyu Jiang, and Qingguo Zhou. "Crop and weed classification based on AutoML." Applied Computing and Intelligence 1, no. 1 (2021): 46–60. http://dx.doi.org/10.3934/aci.2021003.
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<abstract> <p>CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature. However, to manually choose and fine-tune the deep learning models becomes laborious and indispensable in most traditional practices and research. Moreover, the classic objective functions are not thoroughly compatible with agricultural farming tasks as the corresponding models suffer from misclassifying crop to weed, often more likely than in other deep learning application domains. In this paper, we applied autonomous machine learning with a new objective function for crop and weed classification, achieving higher accuracy and lower crop killing rate (rate of identifying a crop as a weed). The experimental results show that our method outperforms state-of-the-art applications, for example, ResNet and VGG19.</p> </abstract>
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Schwen, Lars Ole, Daniela Schacherer, Christian Geißler, and André Homeyer. "Evaluating generic AutoML tools for computational pathology." Informatics in Medicine Unlocked 29 (2022): 100853. http://dx.doi.org/10.1016/j.imu.2022.100853.
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Agrapetidou, Anna, Paulos Charonyktakis, Periklis Gogas, Theophilos Papadimitriou, and Ioannis Tsamardinos. "An AutoML application to forecasting bank failures." Applied Economics Letters 28, no. 1 (February 3, 2020): 5–9. http://dx.doi.org/10.1080/13504851.2020.1725230.
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Wang, Kuan, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han. "Hardware-Centric AutoML for Mixed-Precision Quantization." International Journal of Computer Vision 128, no. 8-9 (June 11, 2020): 2035–48. http://dx.doi.org/10.1007/s11263-020-01339-6.
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Raj, Rishi, Jimson Mathew, Santhosh Kumar Kannath, and Jeny Rajan. "StrokeViT with AutoML for brain stroke classification." Engineering Applications of Artificial Intelligence 119 (March 2023): 105772. http://dx.doi.org/10.1016/j.engappai.2022.105772.
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