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Academic literature on the topic 'Bayesian Machine Learning (BML)'
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Journal articles on the topic "Bayesian Machine Learning (BML)"
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Rigueira, Xurxo, María Pazo, María Araújo, Saki Gerassis, and Elvira Bocos. "Bayesian Machine Learning and Functional Data Analysis as a Two-Fold Approach for the Study of Acid Mine Drainage Events." Water 15, no. 8 (2023): 1553. http://dx.doi.org/10.3390/w15081553.
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Acid mine drainage events have a negative influence on the water quality of fluvial systems affected by coal mining activities. This research focuses on the analysis of these events, revealing hidden correlations among potential factors that contribute to the occurrence of atypical measures and ultimately proposing the basis of an analytical tool capable of automatically capturing the overall behavior of the fluvial system. For this purpose, the hydrological and water quality data collected by an automated station located in a coal mining region in the NW of Spain (Fabero) were analyzed with advanced mathematical methods: statistical Bayesian machine learning (BML) and functional data analysis (FDA). The Bayesian analysis describes a structure fully dedicated to explaining the behavior of the fluvial system and the characterization of the pH, delving into its statistical association with the rest of the variables in the model. FDA allows the definition of several time-dependent correlations between the functional outliers of different variables, namely, the inverse relationship between pH, rainfall, and flow. The results demonstrate that an analytical tool structured around a Bayesian model and functional analysis automatically captures different patterns of the pH in the fluvial system and identifies the underlying anomalies.
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Mobiny, Aryan, Aditi Singh, and Hien Van Nguyen. "Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis." Journal of Clinical Medicine 8, no. 8 (2019): 1241. http://dx.doi.org/10.3390/jcm8081241.
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Knowing when a machine learning system is not confident about its prediction is crucial in medical domains where safety is critical. Ideally, a machine learning algorithm should make a prediction only when it is highly certain about its competency, and refer the case to physicians otherwise. In this paper, we investigate how Bayesian deep learning can improve the performance of the machine–physician team in the skin lesion classification task. We used the publicly available HAM10000 dataset, which includes samples from seven common skin lesion categories: Melanoma (MEL), Melanocytic Nevi (NV), Basal Cell Carcinoma (BCC), Actinic Keratoses and Intraepithelial Carcinoma (AKIEC), Benign Keratosis (BKL), Dermatofibroma (DF), and Vascular (VASC) lesions. Our experimental results show that Bayesian deep networks can boost the diagnostic performance of the standard DenseNet-169 model from 81.35% to 83.59% without incurring additional parameters or heavy computation. More importantly, a hybrid physician–machine workflow reaches a classification accuracy of 90 % while only referring 35 % of the cases to physicians. The findings are expected to generalize to other medical diagnosis applications. We believe that the availability of risk-aware machine learning methods will enable a wider adoption of machine learning technology in clinical settings.
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Oladyshkin, Sergey, Farid Mohammadi, Ilja Kroeker, and Wolfgang Nowak. "Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory." Entropy 22, no. 8 (2020): 890. http://dx.doi.org/10.3390/e22080890.
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Gaussian process emulators (GPE) are a machine learning approach that replicates computational demanding models using training runs of that model. Constructing such a surrogate is very challenging and, in the context of Bayesian inference, the training runs should be well invested. The current paper offers a fully Bayesian view on GPEs for Bayesian inference accompanied by Bayesian active learning (BAL). We introduce three BAL strategies that adaptively identify training sets for the GPE using information-theoretic arguments. The first strategy relies on Bayesian model evidence that indicates the GPE’s quality of matching the measurement data, the second strategy is based on relative entropy that indicates the relative information gain for the GPE, and the third is founded on information entropy that indicates the missing information in the GPE. We illustrate the performance of our three strategies using analytical- and carbon-dioxide benchmarks. The paper shows evidence of convergence against a reference solution and demonstrates quantification of post-calibration uncertainty by comparing the introduced three strategies. We conclude that Bayesian model evidence-based and relative entropy-based strategies outperform the entropy-based strategy because the latter can be misleading during the BAL. The relative entropy-based strategy demonstrates superior performance to the Bayesian model evidence-based strategy.
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Zhou, Ting, Xiaohu Wen, Qi Feng, Haijiao Yu, and Haiyang Xi. "Bayesian Model Averaging Ensemble Approach for Multi-Time-Ahead Groundwater Level Prediction Combining the GRACE, GLEAM, and GLDAS Data in Arid Areas." Remote Sensing 15, no. 1 (2022): 188. http://dx.doi.org/10.3390/rs15010188.
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Accurate groundwater level (GWL) prediction is essential for the sustainable management of groundwater resources. However, the prediction of GWLs remains a challenge due to insufficient data and the complicated hydrogeological system. In this study, we investigated the ability of the Gravity Recovery and Climate Experiment (GRACE) satellite data, the Global Land Evaporation Amsterdam Model (GLEAM) data, the Global Land Data Assimilation System (GLDAS) data, and the publicly available meteorological data in 1-, 2-, and 3-month-ahead GWL prediction using three traditional machine learning models (extreme learning machine, ELM; support vector machine, SVR; and random forest, RF). Meanwhile, we further developed the Bayesian model averaging (BMA) by combining the ELM, SVR, and RF models to avoid the uncertainty of the single models and to improve the predicting accuracy. The validity of the forcing data and the BMA model were assessed for three GWL monitoring wells in the Zhangye Basin in Northwest China. The results indicated that the applied forcing data could be treated as validated inputs to predict the GWL up to 3 months ahead due to the achieved high accuracy of the machine learning models (NS > 0.55). The BMA model could significantly improve the performance of the single machine learning models. Overall, the BMA model reduced the RMSE of the ELM, SVR, and RF models in the testing period by about 13.75%, 24.01%, and 17.69%, respectively; while it improved the NS by about 8.32%, 16.13%, and 9.67% for 1-, 2-, and 3-month-ahead GWL prediction, respectively. The uncertainty analysis results also verified the reliability of the BMA model in multi-time-ahead GWL predicting. This highlighted the efficiency of the satellite data, satellite-based data, and publicly available data as substitute inputs in machine-learning-based GWL prediction, particularly for areas with insufficient or missing data. Meanwhile, the BMA ensemble strategy can serve as a powerful and reliable approach in multi-time-ahead GWL prediction when risk-based decision making is needed or a lack of relevant hydrogeological data impedes the application of the physical models.
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Kim, Sungwon, Meysam Alizamir, Nam Won Kim, and Ozgur Kisi. "Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series." Sustainability 12, no. 22 (2020): 9720. http://dx.doi.org/10.3390/su12229720.
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Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. The addressed research investigates the potential of novel ensemble approach, Bayesian model averaging (BMA), in streamflow forecasting using daily time series data from two stations (i.e., Hongcheon and Jucheon), South Korea. Six categories (i.e., M1–M6) of input combination using different antecedent times were employed for streamflow forecasting. The outcomes of BMA model were compared with those of multivariate adaptive regression spline (MARS), M5 model tree (M5Tree), and Kernel extreme learning machines (KELM) models considering four assessment indexes, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and mean absolute error (MAE). The results revealed the superior accuracy of BMA model over three machine learning models in daily streamflow forecasting. Considering RMSE values among the best models during testing phase, the best BMA model (i.e., BMA2) enhanced the forecasting accuracy of MARS1, M5Tree4, and KELM3 models by 5.2%, 5.8%, and 3.4% in Hongcheon station. Additionally, the best BMA model (i.e., BMA1) improved the forecasting accuracy of MARS1, M5Tree1, and KELM1 models by 6.7%, 9.5%, and 3.7% in Jucheon station. In addition, the best BMA models in both stations allowed the uncertainty estimation, and produced higher uncertainty of peak flows compared to that of low flows. As one of the most robust and effective tools, therefore, the BMA model can be successfully employed for streamflow forecasting with different antecedent times.
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Najafi, Mohammad Reza, Zahra Kavianpour, Banafsheh Najafi, Mohammad Reza Kavianpour, and Hamid Moradkhani. "Air demand in gated tunnels – a Bayesian approach to merge various predictions." Journal of Hydroinformatics 14, no. 1 (2011): 152–66. http://dx.doi.org/10.2166/hydro.2011.108.
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High flowrate through gated tunnels may cause critical flow conditions, especially downstream of the regulating gates. Aeration is found to be the most effective and efficient way to prevent cavitation attack. Several experimental equations are presented to predict air demand in gated tunnels; however, they are restricted to particular model geometries and flow conditions and often provide differing results. In this study the current relationships are first evaluated, and then other approaches for air discharge estimation are investigated. Three machine learning techniques are compared based on the flow measurements of eight physical models, with scales ranging from 1:12–1:20, including the fuzzy inference system (FIS), the genetic fuzzy system (GFS), and the adaptive network-based fuzzy inference system (ANFIS). The Bayesian Model Average (BMA) method is then proposed as a tool to merge the simulations from all models. The BMA provides the weighted average of the predictions, by assigning weights to each model in a probabilistic approach. The application of the BMA is found to be useful for improving the design of hydraulic structures by combining different models and experimental equations.
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Xu, Ren, Nengcheng Chen, Yumin Chen, and Zeqiang Chen. "Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin." Advances in Meteorology 2020 (March 9, 2020): 1–17. http://dx.doi.org/10.1155/2020/8680436.
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Downscaling considerably alleviates the drawbacks of regional climate simulation by general circulation models (GCMs). However, little information is available regarding the downscaling using machine learning methods, specifically at hydrological basin scale. This study developed multiple machine learning (ML) downscaling models, based on a Bayesian model average (BMA), to downscale the precipitation simulation of 8 Coupled Model Intercomparison Project Phase 5 (CMIP5) models using model output statistics (MOS) for the years 1961–2005 in the upper Han River basin. A series of statistical metrics, including Pearson’s correlation coefficient (PCC), root mean squared error (RMSE), and relative bias (Rbias), were used for evaluation and comparative analyses. Moreover, the BMA and the best ML downscaling model were used to downscale precipitation in the 21st century under Representative Concentration Pathway 4.5 (RCP4.5) and RCP8.5 scenarios. The results show the following: (1) The performance of the BMA ensemble simulation is clearly better than that of the individual models and the simple mean model ensemble (MME). The PCC reaches 0.74, and the RMSE is reduced by 28%–60% for all the GCMs and 33% compared to the MME. (2) The downscaled models greatly improved station simulation performance. Support vector machine for regression (SVR) was superior to multilayer perceptron (MLP) and random forest (RF). The downscaling results based on the BMA ensemble simulation and SVR models were regarded as the best performing overall (PCC, RMSE, and Rbias were 0.82, 35.07, mm and −5.45%, respectively). (3) Based on BMA and SVR models, the projected precipitations show a weak increasing trend on the whole under RCP4.5 and RCP8.5. Specifically, the average rainfall during the mid- (2040–2069) and late (2070–2099) 21st century increased by 3.23% and 1.02%, respectively, compared to the base year (1971–2000) under RCP4.5, while they increased by 4.25% and 8.30% under RCP8.5. Additionally, the magnitude of changes during winter and spring was higher than that during summer and autumn. Furthermore, future work is recommended to study the improvement of downscaling models and the effect of local climate.
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Shu, Meiyan, Shuaipeng Fei, Bingyu Zhang, et al. "Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits." Plant Phenomics 2022 (August 28, 2022): 1–17. http://dx.doi.org/10.34133/2022/9802585.
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High-throughput estimation of phenotypic traits from UAV (unmanned aerial vehicle) images is helpful to improve the screening efficiency of breeding maize. Accurately estimating phenotyping traits of breeding maize at plot scale helps to promote gene mining for specific traits and provides a guarantee for accelerating the breeding of superior varieties. Constructing an efficient and accurate estimation model is the key to the application of UAV-based multiple sensors data. This study aims to apply the ensemble learning model to improve the feasibility and accuracy of estimating maize phenotypic traits using UAV-based red-green-blue (RGB) and multispectral sensors. The UAV images of four growth stages were obtained, respectively. The reflectance of visible light bands, canopy coverage, plant height (PH), and texture information were extracted from RGB images, and the vegetation indices were calculated from multispectral images. We compared and analyzed the estimation accuracy of single-type feature and multiple features for LAI (leaf area index), fresh weight (FW), and dry weight (DW) of maize. The basic models included ridge regression (RR), support vector machine (SVM), random forest (RF), Gaussian process (GP), and K-neighbor network (K-NN). The ensemble learning models included stacking and Bayesian model averaging (BMA). The results showed that the ensemble learning model improved the accuracy and stability of maize phenotypic traits estimation. Among the features extracted from UAV RGB images, the highest accuracy was obtained by the combination of spectrum, structure, and texture features. The model had the best accuracy constructed using all features of two sensors. The estimation accuracies of ensemble learning models, including stacking and BMA, were higher than those of the basic models. The coefficient of determination (R2) of the optimal validation results were 0.852, 0.888, and 0.929 for LAI, FW, and DW, respectively. Therefore, the combination of UAV-based multisource data and ensemble learning model could accurately estimate phenotyping traits of breeding maize at plot scale.
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Quadeer, Ahmed A., Matthew R. McKay, John P. Barton, and Raymond H. Y. Louie. "MPF–BML: a standalone GUI-based package for maximum entropy model inference." Bioinformatics 36, no. 7 (2019): 2278–79. http://dx.doi.org/10.1093/bioinformatics/btz925.
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Abstract Summary Learning underlying correlation patterns in data is a central problem across scientific fields. Maximum entropy models present an important class of statistical approaches for addressing this problem. However, accurately and efficiently inferring model parameters are a major challenge, particularly for modern high-dimensional applications such as in biology, for which the number of parameters is enormous. Previously, we developed a statistical method, minimum probability flow–Boltzmann Machine Learning (MPF–BML), for performing fast and accurate inference of maximum entropy model parameters, which was applied to genetic sequence data to estimate the fitness landscape for the surface proteins of human immunodeficiency virus and hepatitis C virus. To facilitate seamless use of MPF–BML and encourage more widespread application to data in diverse fields, we present a standalone cross-platform package of MPF–BML which features an easy-to-use graphical user interface. The package only requires the input data (protein sequence data or data of multiple configurations of a complex system with large number of variables) and returns the maximum entropy model parameters. Availability and implementation The MPF–BML software is publicly available under the MIT License at https://github.com/ahmedaq/MPF-BML-GUI. Supplementary information Supplementary data are available at Bioinformatics online.
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Soria-Olivas, E., J. Gomez-Sanchis, J. D. Martin, et al. "BELM: Bayesian Extreme Learning Machine." IEEE Transactions on Neural Networks 22, no. 3 (2011): 505–9. http://dx.doi.org/10.1109/tnn.2010.2103956.
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