Academic literature on the topic 'Interpretable ML'
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Journal articles on the topic "Interpretable ML"
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Zytek, Alexandra, Ignacio Arnaldo, Dongyu Liu, Laure Berti-Equille, and Kalyan Veeramachaneni. "The Need for Interpretable Features." ACM SIGKDD Explorations Newsletter 24, no. 1 (June 2, 2022): 1–13. http://dx.doi.org/10.1145/3544903.3544905.
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Through extensive experience developing and explaining machine learning (ML) applications for real-world domains, we have learned that ML models are only as interpretable as their features. Even simple, highly interpretable model types such as regression models can be difficult or impossible to understand if they use uninterpretable features. Different users, especially those using ML models for decision-making in their domains, may require different levels and types of feature interpretability. Furthermore, based on our experiences, we claim that the term "interpretable feature" is not specific nor detailed enough to capture the full extent to which features impact the usefulness of ML explanations. In this paper, we motivate and discuss three key lessons: 1) more attention should be given to what we refer to as the interpretable feature space, or the state of features that are useful to domain experts taking real-world actions, 2) a formal taxonomy is needed of the feature properties that may be required by these domain experts (we propose a partial taxonomy in this paper), and 3) transforms that take data from the model-ready state to an interpretable form are just as essential as traditional ML transforms that prepare features for the model.
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Wu, Bozhi, Sen Chen, Cuiyun Gao, Lingling Fan, Yang Liu, Weiping Wen, and Michael R. Lyu. "Why an Android App Is Classified as Malware." ACM Transactions on Software Engineering and Methodology 30, no. 2 (March 2021): 1–29. http://dx.doi.org/10.1145/3423096.
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Machine learning–(ML) based approach is considered as one of the most promising techniques for Android malware detection and has achieved high accuracy by leveraging commonly used features. In practice, most of the ML classifications only provide a binary label to mobile users and app security analysts. However, stakeholders are more interested in the reason why apps are classified as malicious in both academia and industry. This belongs to the research area of interpretable ML but in a specific research domain (i.e., mobile malware detection). Although several interpretable ML methods have been exhibited to explain the final classification results in many cutting-edge Artificial Intelligent–based research fields, until now, there is no study interpreting why an app is classified as malware or unveiling the domain-specific challenges. In this article, to fill this gap, we propose a novel and interpretable ML-based approach (named XMal ) to classify malware with high accuracy and explain the classification result meanwhile. (1) The first classification phase of XMal hinges multi-layer perceptron and attention mechanism and also pinpoints the key features most related to the classification result. (2) The second interpreting phase aims at automatically producing neural language descriptions to interpret the core malicious behaviors within apps. We evaluate the behavior description results by leveraging a human study and an in-depth quantitative analysis. Moreover, we further compare XMal with the existing interpretable ML-based methods (i.e., Drebin and LIME) to demonstrate the effectiveness of XMal . We find that XMal is able to reveal the malicious behaviors more accurately. Additionally, our experiments show that XMal can also interpret the reason why some samples are misclassified by ML classifiers. Our study peeks into the interpretable ML through the research of Android malware detection and analysis.
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Yang, Ziduo, Weihe Zhong, Lu Zhao, and Calvin Yu-Chian Chen. "ML-DTI: Mutual Learning Mechanism for Interpretable Drug–Target Interaction Prediction." Journal of Physical Chemistry Letters 12, no. 17 (April 27, 2021): 4247–61. http://dx.doi.org/10.1021/acs.jpclett.1c00867.
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Lin, Zhiqing. "A Methodological Review of Machine Learning in Applied Linguistics." English Language Teaching 14, no. 1 (December 23, 2020): 74. http://dx.doi.org/10.5539/elt.v14n1p74.
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The traditional linear regression in applied linguistics (AL) suffers from the drawbacks arising from the strict assumptions namely: linearity, and normality, etc. More advanced methods are needed to overcome the shortcomings of the traditional method and grapple with intricate linguistic problems. However, there is no previous review on the applications of machine learning (ML) in AL, the introduction of interpretable ML, and related practical software. This paper addresses these gaps by reviewing the representative algorithms of ML in AL. The result shows that ML is applicable in AL and enjoys a promising future. It goes further to discuss the applications of interpretable ML for reporting the results in AL. Finally, it ends with the recommendations of the practical programming languages, software, and platforms to implement ML for researchers in AL to foster the interdisciplinary studies between AL and ML.
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Abdullah, Talal A. A., Mohd Soperi Mohd Zahid, and Waleed Ali. "A Review of Interpretable ML in Healthcare: Taxonomy, Applications, Challenges, and Future Directions." Symmetry 13, no. 12 (December 17, 2021): 2439. http://dx.doi.org/10.3390/sym13122439.
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We have witnessed the impact of ML in disease diagnosis, image recognition and classification, and many more related fields. Healthcare is a sensitive field related to people’s lives in which decisions need to be carefully taken based on solid evidence. However, most ML models are complex, i.e., black-box, meaning they do not provide insights into how the problems are solved or why such decisions are proposed. This lack of interpretability is the main reason why some ML models are not widely used yet in real environments such as healthcare. Therefore, it would be beneficial if ML models could provide explanations allowing physicians to make data-driven decisions that lead to higher quality service. Recently, several efforts have been made in proposing interpretable machine learning models to become more convenient and applicable in real environments. This paper aims to provide a comprehensive survey and symmetry phenomena of IML models and their applications in healthcare. The fundamental characteristics, theoretical underpinnings needed to develop IML, and taxonomy for IML are presented. Several examples of how they are applied in healthcare are investigated to encourage and facilitate the use of IML models in healthcare. Furthermore, current limitations, challenges, and future directions that might impact applying ML in healthcare are addressed.
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Sajid, Mirza Rizwan, Arshad Ali Khan, Haitham M. Albar, Noryanti Muhammad, Waqas Sami, Syed Ahmad Chan Bukhari, and Iram Wajahat. "Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score." Computational Intelligence and Neuroscience 2022 (May 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/5475313.
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Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by practitioners and has become a significant hindrance in their application. Therefore, in highly sensitive decisions, black boxes of ML models are not recommended. We proposed a novel methodology that uses complex supervised ML models and transforms them into simple, interpretable, transparent statistical models. This methodology is like stacking ensemble ML in which the best ML models are used as a base learner to compute relative feature weights. The index of these weights is further used as a single covariate in the simple logistic regression model to estimate the likelihood of an event. We tested this methodology on the primary dataset related to cardiovascular diseases (CVDs), the leading cause of mortalities in recent times. Therefore, early risk assessment is an important dimension that can potentially reduce the burden of CVDs and their related mortality through accurate but interpretable risk prediction models. We developed an artificial neural network and support vector machines based on ML models and transformed them into a simple statistical model and heart risk scores. These simplified models were found transparent, reliable, valid, interpretable, and approximate in predictions. The findings of this study suggest that complex supervised ML models can be efficiently transformed into simple statistical models that can also be validated.
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Singh, Devesh. "Interpretable Machine-Learning Approach in Estimating FDI Inflow: Visualization of ML Models with LIME and H2O." TalTech Journal of European Studies 11, no. 1 (May 1, 2021): 133–52. http://dx.doi.org/10.2478/bjes-2021-0009.
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Abstract In advancement of interpretable machine learning (IML), this research proposes local interpretable model-agnostic explanations (LIME) as a new visualization technique in a novel informative way to analyze the foreign direct investment (FDI) inflow. This article examines the determinants of FDI inflow through IML with a supervised learning method to analyze the foreign investment determinants in Hungary by using an open-source artificial intelligence H2O platform. This author used three ML algorithms—general linear model (GML), gradient boosting machine (GBM), and random forest (RF) classifier—to analyze the FDI inflow from 2001 to 2018. The result of this study shows that in all three classifiers GBM performs better to analyze FDI inflow determinants. The variable value of production in a region is the most influenced determinant to the inflow of FDI in Hungarian regions. Explanatory visualizations are presented from the analyzed dataset, which leads to their use in decision-making.
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Carreiro Pinasco, Gustavo, Eduardo Moreno Júdice de Mattos Farina, Fabiano Novaes Barcellos Filho, Willer França Fiorotti, Matheus Coradini Mariano Ferreira, Sheila Cristina de Souza Cruz, Andre Louzada Colodette, et al. "An interpretable machine learning model for covid-19 screening." Journal of Human Growth and Development 32, no. 2 (June 23, 2022): 268–74. http://dx.doi.org/10.36311/jhgd.v32.13324.
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Introduction: the Coronavirus Disease 2019 (COVID-19) is a viral disease which has been declared a pandemic by the WHO. Diagnostic tests are expensive and are not always available. Researches using machine learning (ML) approach for diagnosing SARS-CoV-2 infection have been proposed in the literature to reduce cost and allow better control of the pandemic. Objective: we aim to develop a machine learning model to predict if a patient has COVID-19 with epidemiological data and clinical features. Methods: we used six ML algorithms for COVID-19 screening through diagnostic prediction and did an interpretative analysis using SHAP models and feature importances. Results: our best model was XGBoost (XGB) which obtained an area under the ROC curve of 0.752, a sensitivity of 90%, a specificity of 40%, a positive predictive value (PPV) of 42.16%, and a negative predictive value (NPV) of 91.0%. The best predictors were fever, cough, history of international travel less than 14 days ago, male gender, and nasal congestion, respectively. Conclusion: We conclude that ML is an important tool for screening with high sensitivity, compared to rapid tests, and can be used to empower clinical precision in COVID-19, a disease in which symptoms are very unspecific.
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Menon, P. Archana, and Dr R. Gunasundari. "Study of Interpretability in ML Algorithms for Disease Prognosis." Revista Gestão Inovação e Tecnologias 11, no. 4 (August 19, 2021): 4735–49. http://dx.doi.org/10.47059/revistageintec.v11i4.2500.
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Disease prognosis plays an important role in healthcare. Diagnosing disease at an early stage is crucial to provide treatment to the patient at the earliest in order to save his/her life or to at least reduce the severity of the disease. Application of Machine Learning algorithms is a promising area for the early and accurate diagnosis of chronic diseases. The black-box approach of Machine Learning models has been circumvented by providing different Interpretability methods. The importance of interpretability in health care field especially while taking decisions in life threatening diseases is crucial. Interpretable model increases the confidence of a medical practitioner in taking decisions. This paper gives an insight to the importance of explanations as well as the interpretability methods applied to different machine learning and deep learning models developed in recent years.
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Dawid, Anna, Patrick Huembeli, Michał Tomza, Maciej Lewenstein, and Alexandre Dauphin. "Hessian-based toolbox for reliable and interpretable machine learning in physics." Machine Learning: Science and Technology 3, no. 1 (November 24, 2021): 015002. http://dx.doi.org/10.1088/2632-2153/ac338d.
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Abstract Machine learning (ML) techniques applied to quantum many-body physics have emerged as a new research field. While the numerical power of this approach is undeniable, the most expressive ML algorithms, such as neural networks, are black boxes: The user does neither know the logic behind the model predictions nor the uncertainty of the model predictions. In this work, we present a toolbox for interpretability and reliability, agnostic of the model architecture. In particular, it provides a notion of the influence of the input data on the prediction at a given test point, an estimation of the uncertainty of the model predictions, and an extrapolation score for the model predictions. Such a toolbox only requires a single computation of the Hessian of the training loss function. Our work opens the road to the systematic use of interpretability and reliability methods in ML applied to physics and, more generally, science.
Dissertations / Theses on the topic "Interpretable ML"
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Gustafsson, Sebastian. "Interpretable serious event forecasting using machine learning and SHAP." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444363.
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Accurate forecasts are vital in multiple areas of economic, scientific, commercial, and industrial activity. There are few previous studies on using forecasting methods for predicting serious events. This thesis set out to investigate two things, firstly whether machine learning models could be applied to the objective of forecasting serious events. Secondly, if the models could be made interpretable. Given these objectives, the approach was to formulate two forecasting tasks for the models and then use the Python framework SHAP to make them interpretable. The first task was to predict if a serious event will happen in the coming eight hours. The second task was to forecast how many serious events that will happen in the coming six hours. GBDT and LSTM models were implemented, evaluated, and compared on both tasks. Given the problem complexity of forecasting, the results match those of previous related research. On the classification task, the best performing model achieved an accuracy of 71.6%, and on the regression task, it missed by less than 1 on average.Exakta prognoser är viktiga inom flera områden av ekonomisk, vetenskaplig, kommersiell och industriell verksamhet. Det finns få tidigare studier där man använt prognosmetoder för att förutsäga allvarliga händelser. Denna avhandling syftar till att undersöka två saker, för det första om maskininlärningsmodeller kan användas för att förutse allvarliga händelser. För det andra, om modellerna kunde göras tolkbara. Med tanke på dessa mål var metoden att formulera två prognosuppgifter för modellerna och sedan använda Python-ramverket SHAP för att göra dem tolkbara. Den första uppgiften var att förutsäga om en allvarlig händelse kommer att ske under de kommande åtta timmarna. Den andra uppgiften var att förutse hur många allvarliga händelser som kommer att hända under de kommande sex timmarna. GBDT- och LSTM-modeller implementerades, utvärderades och jämfördes för båda uppgifterna. Med tanke på problemkomplexiteten i att förutspå framtiden matchar resultaten de från tidigare relaterad forskning. På klassificeringsuppgiften uppnådde den bäst presterande modellen en träffsäkerhet på 71,6%, och på regressionsuppgiften missade den i genomsnitt med mindre än 1 i antal förutspådda allvarliga händelser.
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Gilmore, Eugene M. "Learning Interpretable Decision Tree Classifiers with Human in the Loop Learning and Parallel Coordinates." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/418633.
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The Machine Learning (ML) community has recently started to recognise the importance of model interpretability when using ML techniques. In this work, I review the literature on Explainable Artificial Intelligence (XAI) and interpretability in ML and discuss several reasons why interpretability is critical for many ML applications. Although there is now increased interest in XAI, there are significant issues with the approaches taken in a large portion of the research in XAI. In particular, the popularity of techniques that try to explain black-box models often leads to misleading explanations that are not faithful to the model being explained.
The popularity of black-box models is, in large part, due to the immense size and complexity of many datasets available today. The high dimensionality of many datasets has encouraged research in ML and particular techniques such as Artificial Neural Networks (ANNs). However, I argue in this work that the high dimensionality of a dataset should not, in itself, be a reason to settle for black-box models that humans cannot understand. Instead, I argue for the need to learn inherently interpretable models, rather than black-box models with post-hoc explanations of their results.
One of the most well-known ML models for supervised learning tasks that remains interpretable to humans is the Decision Tree Classifier (DTC). The DTC's interpretability is due to its simple tree structure where a human can individually inspect the splits at each node in the tree. Although a DTC's fundamental structure is interpretable to humans, even a DTC can effective become a black-box model. This may be due to the size of a DTC being too large for a human to comprehend. Alternatively, a DTC may use uninterpretable oblique splits at each node. These oblique splits most often use a hyperplane through the entire attributes space of a dataset to construct a split which is impossible for a human to interpret past three dimensions.
In this work, I propose techniques for learning and visualising DTCs and datasets to produce interpretable classifiers that do not sacrifice predictive power. Moreover, I combine such visualisation with an interactive DTC building strategy and enable productive and effective Human-In-the-Loop-Learning (HILL). Not only do classifiers learnt with human involvement have the natural requirement of being humanly interpretable, but there are also several additional advantages to be gained by involving human expertise. These advantages include the ability for a domain expert to contribute their domain knowledge to a model. We can also exploit the highly sophisticated visual pattern recognition capabilities of the human to learn models that more effectively generalise to unseen data. Despite limitations of current HILL systems, a user study conducted as part of this work provides promising results for the involving the human in the construction of DTCs.
However, to effective employ this learning style, we need powerful visualisation techniques for both high dimensional datasets and DTCs. Remarkably, despite being ideally suited for high dimensional datasets, the use of Parallel Coordinates (||-coords) by the ML community is minimal. First proposed by Alfred Inselberg, ||-coords is a revolutionary visualisation technique that uses parallel axis to display a dataset of an arbitrary number of dimensions.
Using ||-coords, I propose a HILL system for the construction of DTCs. This work also exploits the ||-coords visualisation system to facilitate human input to the splits of internal nodes in a DTC. In addition, I propose a new form of oblique split for DTCs that uses the properties of the ||-coords plane. Unlike other oblique rules, this oblique rule can be easily visualised using ||-coords.
While there has recently been renewed interest in XAI and HILL, the research that evaluates systems that facilitate XAI and HILL is limited. I report on an online survey that gathers data from 104 participants. This survey examines participants' use of visualisation systems which I argue are ideally suited for HILL and XAI. The results support my hypothesis and the proposals for HILL.
I further argue that for a HILL system to succeed, comprehensive algorithm support is critical. As such, I propose two new DTC induction algorithms. These algorithms are designed to be used in conjunction with the HILL system developed in this work to provide algorithmic assistance in the form of suggestions of splits for a DTC node. The first proposed induction algorithm uses the newly proposed form of oblique split with ||-coords to learn interpretable splits that can capture correlations between attributes. The second induction algorithm advances the nested cavities algorithm originally proposed by Inselberg for classification tasks using ||-coords. Using these induction algorithms enables learning of DTCs with oblique splits that remain interpretable to a human without sacrificing predictive performance.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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REPETTO, MARCO. "Black-box supervised learning and empirical assessment: new perspectives in credit risk modeling." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/402366.
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I recenti algoritmi di apprendimento automatico ad alte prestazioni sono convincenti ma opachi, quindi spesso è difficile capire come arrivano alle loro previsioni, dando origine a problemi di interpretabilità.
Questi problemi sono particolarmente rilevanti nell'apprendimento supervisionato, dove questi modelli "black-box" non sono facilmente comprensibili per le parti interessate.
Un numero crescente di lavori si concentra sul rendere più interpretabili i modelli di apprendimento automatico, in particolare quelli di apprendimento profondo.
Gli approcci attualmente proposti si basano su un'interpretazione post-hoc, utilizzando metodi come la mappatura della salienza e le dipendenze parziali.
Nonostante i progressi compiuti, l'interpretabilità è ancora un'area di ricerca attiva e non esiste una soluzione definitiva. Inoltre, nei processi decisionali ad alto rischio, l'interpretabilità post-hoc può essere subottimale.
Un esempio è il campo della modellazione del rischio di credito aziendale.
In questi campi, i modelli di classificazione discriminano tra buoni e cattivi mutuatari.
Di conseguenza, gli istituti di credito possono utilizzare questi modelli per negare le richieste di prestito.
Il rifiuto di un prestito può essere particolarmente dannoso quando il mutuatario non può appellarsi o avere una spiegazione e una motivazione della decisione.
In questi casi, quindi, è fondamentale capire perché questi modelli producono un determinato risultato e orientare il processo di apprendimento verso previsioni basate sui fondamentali.
Questa tesi si concentra sul concetto di Interpretable Machine Learning, con particolare attenzione al contesto della modellazione del rischio di credito.
In particolare, la tesi ruota attorno a tre argomenti: l'interpretabilità agnostica del modello, l'interpretazione post-hoc nel rischio di credito e l'apprendimento guidato dall'interpretabilità.
Più specificamente, il primo capitolo è un'introduzione guidata alle tecniche model-agnostic che caratterizzano l'attuale panorama del Machine Learning e alle loro implementazioni.
Il secondo capitolo si concentra su un'analisi empirica del rischio di credito delle piccole e medie imprese italiane.
Propone una pipeline analitica in cui l'interpretabilità post-hoc gioca un ruolo cruciale nel trovare le basi rilevanti che portano un'impresa al fallimento.
Il terzo e ultimo articolo propone una nuova metodologia di iniezione di conoscenza multicriteriale.
La metodologia si basa sulla doppia retropropagazione e può migliorare le prestazioni del modello, soprattutto in caso di scarsità di dati.
Il vantaggio essenziale di questa metodologia è che permette al decisore di imporre le sue conoscenze pregresse all'inizio del processo di apprendimento, facendo previsioni che si allineano con i fondamentali.Recent highly performant Machine Learning algorithms are compelling but opaque, so it is often hard to understand how they arrive at their predictions giving rise to interpretability issues. Such issues are particularly relevant in supervised learning, where such black-box models are not easily understandable by the stakeholders involved. A growing body of work focuses on making Machine Learning, particularly Deep Learning models, more interpretable. The currently proposed approaches rely on post-hoc interpretation, using methods such as saliency mapping and partial dependencies. Despite the advances that have been made, interpretability is still an active area of research, and there is no silver bullet solution. Moreover, in high-stakes decision-making, post-hoc interpretability may be sub-optimal. An example is the field of enterprise credit risk modeling. In such fields, classification models discriminate between good and bad borrowers. As a result, lenders can use these models to deny loan requests. Loan denial can be especially harmful when the borrower cannot appeal or have the decision explained and grounded by fundamentals. Therefore in such cases, it is crucial to understand why these models produce a given output and steer the learning process toward predictions based on fundamentals. This dissertation focuses on the concept of Interpretable Machine Learning, with particular attention to the context of credit risk modeling. In particular, the dissertation revolves around three topics: model agnostic interpretability, post-hoc interpretation in credit risk, and interpretability-driven learning. More specifically, the first chapter is a guided introduction to the model-agnostic techniques shaping today’s landscape of Machine Learning and their implementations. The second chapter focuses on an empirical analysis of the credit risk of Italian Small and Medium Enterprises. It proposes an analytical pipeline in which post-hoc interpretability plays a crucial role in finding the relevant underpinnings that drive a firm into bankruptcy. The third and last paper proposes a novel multicriteria knowledge injection methodology. The methodology is based on double backpropagation and can improve model performance, especially in the case of scarce data. The essential advantage of such methodology is that it allows the decision maker to impose his previous knowledge at the beginning of the learning process, making predictions that align with the fundamentals.
Book chapters on the topic "Interpretable ML"
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Nandi, Anirban, and Aditya Kumar Pal. "Interpretable ML and Explainable ML Differences." In Interpreting Machine Learning Models, 83–95. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7802-4_7.
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Qiu, Waishan, Wenjing Li, Xun Liu, and Xiaokai Huang. "Subjectively Measured Streetscape Qualities for Shanghai with Large-Scale Application of Computer Vision and Machine Learning." In Proceedings of the 2021 DigitalFUTURES, 242–51. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5983-6_23.
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AbstractRecently, many new studies emerged to apply computer vision (CV) to street view imagery (SVI) dataset to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities. However, human perceptions (e.g., imageability) have a subtle relationship to visual elements which cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain more human behaviors. However, the effectiveness of integrating subjective measures with SVI dataset has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected experts’ rating on sample SVIs regarding the four qualities which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting the scores. We found a strong correlation between predicted complexity score and the density of urban amenities and services Point of Interests (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five renowned urban cores worldwide. Rather than predicting perceptual scores directly from generic image features using convolution neural network, our approach follows what urban design theory suggested and confirms various streetscape features affecting multi-dimensional human perceptions. Therefore, its result provides more interpretable and actionable implications for policymakers and city planners.
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Bagci Das, Duygu, and Derya Birant. "XHAC." In Emerging Trends in IoT and Integration with Data Science, Cloud Computing, and Big Data Analytics, 146–64. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-4186-9.ch008.
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Explainable artificial intelligence (XAI) is a concept that has emerged and become popular in recent years. Even interpretation in machine learning models has been drawing attention. Human activity classification (HAC) systems still lack interpretable approaches. In this study, an approach, called eXplainable HAC (XHAC), was proposed in which the data exploration, model structure explanation, and prediction explanation of the ML classifiers for HAR were examined to improve the explainability of the HAR models' components such as sensor types and their locations. For this purpose, various internet of things (IoT) sensors were considered individually, including accelerometer, gyroscope, and magnetometer. The location of these sensors (i.e., ankle, arm, and chest) was also taken into account. The important features were explored. In addition, the effect of the window size on the classification performance was investigated. According to the obtained results, the proposed approach makes the HAC processes more explainable compared to the black-box ML techniques.
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Sajjadinia, Seyed Shayan, Bruno Carpentieri, and Gerhard A. Holzapfel. "A Pointwise Evaluation Metric to Visualize Errors in Machine Learning Surrogate Models." In Proceedings of CECNet 2021. IOS Press, 2021. http://dx.doi.org/10.3233/faia210386.
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Numerical simulation is widely used to study physical systems, although it can be computationally too expensive. To counter this limitation, a surrogate may be used, which is a high-performance model that replaces the main numerical model by using, e.g., a machine learning (ML) regressor that is trained on a previously generated subset of possible inputs and outputs of the numerical model. In this context, inspired by the definition of the mean squared error (MSE) metric, we introduce the pointwise MSE (PMSE) metric, which can give a better insight into the performance of such ML models over the test set, by focusing on every point that forms the physical system. To show the merits of the metric, we will create a dataset of a physics problem that will be used to train an ML surrogate, which will then be evaluated by the metrics. In our experiment, the PMSE contour demonstrates how the model learns the physics in different model regions and, in particular, the correlation between the characteristics of the numerical model and the learning progress can be observed. We therefore conclude that this simple and efficient metric can provide complementary and potentially interpretable information regarding the performance and functionality of the surrogate.
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Katsuragi, Miki, and Kenji Tanaka. "Dropout Prediction by Interpretable Machine Learning Model Towards Preventing Student Dropout." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220700.
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In the education industry, the needs of online learning are significantly increasing. However, the web-based courses demonstrate higher dropout rates than traditional education courses. As a result, engaging students with data analysis is getting more crucial especially for distance learning. In this study, we analyze data on the daily learning status of students in order to predict the student’s dropout in online schools. Specifically, we trained a dropout prediction machine leaning model with 1) Basic attributes of students, 2) Progress of learning materials, and 3) Slack conversation data between students and teachers. The experimental results show that the accuracy rate of the machine learning model has reached 96.4%. As a result, the model was able to predict 78% of the students who actually dropped out of school. We also looked into feature importance by SHAP value to gain ML model interpretability.
Conference papers on the topic "Interpretable ML"
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Ignatiev, Alexey, Joao Marques-Silva, Nina Narodytska, and Peter J. Stuckey. "Reasoning-Based Learning of Interpretable ML Models." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/608.
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Artificial Intelligence (AI) is widely used in decision making procedures in myriads of real-world applications across important practical areas such as finance, healthcare, education, and safety critical systems. Due to its ubiquitous use in safety and privacy critical domains, it is often vital to understand the reasoning behind the AI decisions, which motivates the need for explainable AI (XAI). One of the major approaches to XAI is represented by computing so-called interpretable machine learning (ML) models, such as decision trees (DT), decision lists (DL) and decision sets (DS). These models build on the use of if-then rules and are thus deemed to be easily understandable by humans. A number of approaches have been proposed in the recent past to devising all kinds of interpretable ML models, the most prominent of which involve encoding the problem into a logic formalism, which is then tackled by invoking a reasoning or discrete optimization procedure. This paper overviews the recent advances of the reasoning and constraints based approaches to learning interpretable ML models and discusses their advantages and limitations.
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Nair, Rahul, Massimiliano Mattetti, Elizabeth Daly, Dennis Wei, Oznur Alkan, and Yunfeng Zhang. "What Changed? Interpretable Model Comparison." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/393.
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We consider the problem of distinguishing two machine learning (ML) models built for the same task in a human-interpretable way. As models can fail or succeed in different ways, classical accuracy metrics may mask crucial qualitative differences. This problem arises in a few contexts. In business applications with periodically retrained models, an updated model may deviate from its predecessor for some segments without a change in overall accuracy. In automated ML systems, where several ML pipelines are generated, the top pipelines have comparable accuracy but may have more subtle differences. We present a method for interpretable comparison of binary classification models by approximating them with Boolean decision rules. We introduce stabilization conditions that allow for the two rule sets to be more directly comparable. A method is proposed to compare two rule sets based on their statistical and semantic similarity by solving assignment problems and highlighting changes. An empirical evaluation on several benchmark datasets illustrates the insights that may be obtained and shows that artificially induced changes can be reliably recovered by our method.
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Preece, Alun, Dan Harborne, Ramya Raghavendra, Richard Tomsett, and Dave Braines. "Provisioning Robust and Interpretable AI/ML-Based Service Bundles." In MILCOM 2018 - IEEE Military Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/milcom.2018.8599838.
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Karatekin, Tamer, Selim Sancak, Gokhan Celik, Sevilay Topcuoglu, Guner Karatekin, Pinar Kirci, and Ali Okatan. "Interpretable Machine Learning in Healthcare through Generalized Additive Model with Pairwise Interactions (GA2M): Predicting Severe Retinopathy of Prematurity." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00020.
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Sim, Rachael Hwee Ling, Xinyi Xu, and Bryan Kian Hsiang Low. "Data Valuation in Machine Learning: "Ingredients", Strategies, and Open Challenges." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/782.
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Data valuation in machine learning (ML) is an emerging research area that studies the worth of data in ML. Data valuation is used in collaborative ML to determine a fair compensation for every data owner and in interpretable ML to identify the most responsible, noisy, or misleading training examples. This paper presents a comprehensive technical survey that provides a new formal study of data valuation in ML through its “ingredients” and the corresponding properties, grounds the discussion of common desiderata satisfied by existing data valuation strategies on our proposed ingredients, and identifies open research challenges for designing new ingredients, data valuation strategies, and cost reduction techniques.
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Izza, Yacine, and Joao Marques-Silva. "On Explaining Random Forests with SAT." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/356.
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Random Forest (RFs) are among the most widely used Machine Learning (ML) classifiers. Even though RFs are not interpretable, there are no dedicated non-heuristic approaches for computing explanations of RFs. Moreover, there is recent work on polynomial algorithms for explaining ML models, including naive Bayes classifiers. Hence, one question is whether finding explanations of RFs can be solved in polynomial time. This paper answers this question negatively, by proving that computing one PI-explanation of an RF is D^P-hard. Furthermore, the paper proposes a propositional encoding for computing explanations of RFs, thus enabling finding PI-explanations with a SAT solver. This contrasts with earlier work on explaining boosted trees (BTs) and neural networks (NNs), which requires encodings based on SMT/MILP. Experimental results, obtained on a wide range of publicly available datasets, demonstrate that the proposed SAT-based approach scales to RFs of sizes common in practical applications. Perhaps more importantly, the experimental results demonstrate that, for the vast majority of examples considered, the SAT-based approach proposed in this paper significantly outperforms existing heuristic approaches.
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Aglin, Gaël, Siegfried Nijssen, and Pierre Schaus. "PyDL8.5: a Library for Learning Optimal Decision Trees." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/750.
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Decision Trees (DTs) are widely used Machine Learning (ML) models with a broad range of applications. The interest in these models has increased even further in the context of Explainable AI (XAI), as decision trees of limited depth are very interpretable models. However, traditional algorithms for learning DTs are heuristic in nature; they may produce trees that are of suboptimal quality under depth constraints. We introduce PyDL8.5, a Python library to infer depth-constrained Optimal Decision Trees (ODTs). PyDL8.5 provides an interface for DL8.5, an efficient algorithm for inferring depth-constrained ODTs. The library provides an easy-to-use scikit-learn compatible interface. It cannot only be used for classification tasks, but also for regression, clustering, and other tasks. We introduce an interface that allows users to easily implement these other learning tasks. We provide a number of examples of how to use this library.
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Kurasova, Olga, Virginijus Marcinkevičius, and Birutė Mikulskienė. "Enhanced Visualization of Customized Manufacturing Data." In WSCG'2021 - 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2021. Západočeská univerzita, 2021. http://dx.doi.org/10.24132/csrn.2021.3002.12.
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Enhanced Visualization of Customized Manufacturing Data Olga Kurasova Mykolas Romeris University Ateities str. 20 LT-08303 Vilnius, Lithuaniaolga.kurasova@mif.vu.lt Virginijus Marcinkevičius Mykolas Romeris University Ateities str. 20 LT-08303 Vilnius, Lithuaniavirginijus.marcinkevicius@mif.vu.lt Birutė MikulskienėMykolas Romeris University Ateities str. 20 LT-08303 Vilnius, Lithuaniabirute.mikulskiene@mruni.eu ABSTRACTRecently, customized manufacturing is gaining much momentum. Consumers do not want mass-produced products but are looking for unique and exclusive ones. It is especially evident in the furniture industry. As it is necessary to set an individual price for each individually manufactured product, companies face the need to quickly estimate a preliminary cost and price as soon as an order is received. The task of estimating costs as precise and timely as possible has become critical in customized manufacturing. The cost estimation problem can be solved as a prediction problem using various machine learning (ML) techniques. In order to obtain more accurate price prediction, it is necessary to delve deeper into the data. Data visualization methods are excellent for this purpose. Moreover, it is necessary to consider that the managers who set the price of the product are not ML experts. Thus, data visualization methods should be integrated into the decision support system. On the one hand, these methods should be simple, easily understandable and interpretable. On the other hand, the methods should include more sophisticated approaches that allowed reveal hidden data structure. Here, dimensionality-reduction methods can be employed. In this paper, we propose a data visualization process that can be useful for data analysis in customized furniture manufacturing to get to know the data better, allowing us to develop enhanced price prediction models.
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Aditama, Prihandono, Tina Koziol, and Dr Meindert Dillen. "Development of an Artificial Intelligence-Based Well Integrity Monitoring Solution." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211093-ms.
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Abstract The question of how to safeguard well integrity is one of the most important problems faced by oil and gas companies today. With the rise of Artificial Intelligence (AI) and Machine Learning (ML), many companies explore new technologies to improve well integrity and avoid catastrophic events. This paper presents the Proof of Concept (PoC) of an AI-based well integrity monitoring solution for gas lift, natural flow, and water injector wells. AI model prototypes were built to detect annulus leakage as incident-relevant anomalies from time series sensor data. The historical well annulus leakage incidents were classified based on well type and the incident relevant anomalies were categorized as short and long-term. The objective of the PoC is to build generalized AI models that could detect historical events and future events in unseen wells. The success criteria are discussed and agreed with the Subject Matter Experts (SMEs). Two statistical metrics were defined (Detected Event Rate – DER – and False Alarm Rate – FAR) to quantitively evaluate the model performance and decide if it could be used for the next phase. The high frequency sensor data were retrieved from the production historian. The importance of the sensor was aligned with the SMEs and only a small number of sensors was used as input variable. The raw data was pre-processed and resampled to improve model performance and increase computational efficiency. Throughout the PoC, the authors learnt that specific AI models needed to be implemented for different well types as generalization across well types could not be achieved. Depending on the number of available labels in the training dataset, either unsupervised or supervised ML models were developed. Deep learning models, based on LSTM (Long-Short Term Memory) autoencoder and classifier were used to detect complex anomalies. In cases where limited data were available and simplistic anomaly patterns were present, deterministic rules were implemented to detect well integrity-relevant incidents. The LIME (Local Interpretable Model-Agnostic Explanations) framework was used to derive the most important sensors causing the anomaly prediction to enable the users to critically validate the AI suggestion. The AI models for gas lift and natural flow wells achieved a sufficient level of performance with a minimum of 75% of historical events detected and less than one false positive per month per well.
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Coutinho, Emilio J. R., and Marcelo J. Aqua and Eduardo Gildin. "Physics-Aware Deep-Learning-Based Proxy Reservoir Simulation Model Equipped with State and Well Output Prediction." In SPE Reservoir Simulation Conference. SPE, 2021. http://dx.doi.org/10.2118/203994-ms.
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Abstract Physics-aware machine learning (ML) techniques have been used to endow data-driven proxy models with features closely related to the ones encountered in nature. Examples span from material balance and conservation laws. Physics-based and data-driven reduced-order models or a combination thereof (hybrid-based models) can lead to fast, reliable, and interpretable simulations used in many reservoir management workflows. We built on a recently developed deep-learning-based reduced-order modeling framework by adding a new step related to information of the input-output behavior (e.g., well rates) of the reservoir and not only the states (e.g., pressure and saturation) matching. A Combination of data-driven model reduction strategies and machine learning (deep- neural networks – NN) will be used here to achieve state and input-output matching simultaneously. In Jin, Liu and Durlofsky (2020), the authors use a NN architecture where it is possible to predict the state variables evolution after training an autoencoder coupled with a control system approach (Embed to Control - E2C) and adding some physical components (Loss functions) to the neural network training procedure. In this paper, we extend this idea by adding the simulation model output, e.g., well bottom-hole pressure and well flowrates, as data to be used in the training procedure. Additionally, we added a new neural network to the E2C transition model to handle the connections between state variables and model outputs. By doing this, it is possible to estimate the evolution in time of both the state variables as well as the output variables simultaneously. The method proposed provides a fast and reliable proxy for the simulation output, which can be applied to a well-control optimization problem. Such a non-intrusive method, like data-driven models, does not need to have access to reservoir simulation internal structure. So it can be easily applied to commercial reservoir simulations. We view this as an analogous step to system identification whereby mappings related to state dynamics, inputs (controls), and measurements (output) are obtained. The proposed method is applied to an oil-water model with heterogeneous permeability, 4 injectors, and 5 producer wells. We used 300 sampled well control sets to train the autoencoder and another set to validate the obtained autoencoder parameters.
Reports on the topic "Interpretable ML"
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Zhu, Qing, William Riley, and James Randerson. Improve wildfire predictability driven by extreme water cycle with interpretable physically-guided ML/AI. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769720.
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