Ensemble learning with member optimization for fault diagnosis of a building energy system

Patton, 1999, Artificial intelligence approaches to fault diagnosis for dynamic systems, Int. J. Appl. Math. Comp., 9, 471 Katipamula, 2005, Methods for fault detection, diagnostics, and prognostics for building systems–a review, part I, Hvac R Res. McGuire, 1988, The development and practical application of rotor dynamic analysis techniques for large steam turbine generators Bently, 1988, Detection of rotor cracks, 129 Yang, 2014, Thermal comfort and building energy consumption implications – a review, Appl. Energy, 115, 164, 10.1016/j.apenergy.2013.10.062 Yu, 2016, Comparative study of the cooling energy performance of variable refrigerant flow systems and variable air volume systems in office buildings, Appl. Energy, 183, 725, 10.1016/j.apenergy.2016.09.033 Haber, 1987, An expert system for building energy consumption analysis: prototype results Vapnik, 1964, A note on class of perceptron, Automation Remote Control., 24 Qin, 2005, A SVM face recognition method based on Gabor-featured key points, Mach. Learn. Cybernet., 8, 5144 Sun, 2002, Web classification using support vector machine, 96 Fan, 2019, Chiller fault diagnosis with field sensors using the technology of imbalanced data, Appl. Thermal Eng., 159, 10.1016/j.applthermaleng.2019.113933 Ren, 2008, Fault diagnosis strategy for incompletely described samples and its application to refrigeration system, Mech. Syst. Signal Process., 22, 436, 10.1016/j.ymssp.2007.08.004 Han, 2011, Automated FDD of multiple-simultaneous faults (MSF) and the application to building chillers, Energy Build., 43, 2524, 10.1016/j.enbuild.2011.06.011 Han, 2019, Least squares support vector machine (LS-SVM)-based chiller fault diagnosis using fault indicative features, Appl. Thermal Eng., 154, 540, 10.1016/j.applthermaleng.2019.03.111 Katipamula, 2005, Review article: methods for fault detection, diagnostics, and prognostics for building systems—a review, part II, HVAC&R Res., 11, 169, 10.1080/10789669.2005.10391133 Lecun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Guo, 2018, Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving, Appl. Energy, 225, 732, 10.1016/j.apenergy.2018.05.075 Zhao, 2014, Field implementation and evaluation of a decoupling-based fault detection and diagnostic method for chillers, Energy Build., 72, 419, 10.1016/j.enbuild.2014.01.003 Cui, 2005, A model-based online fault detection and diagnosis strategy for centrifugal chiller systems, Int. J. Thermal Sci., 44, 986, 10.1016/j.ijthermalsci.2005.03.004 Li, 2014, Diffusion maps based k-nearest-neighbor rule technique for semiconductor manufacturing process fault detection, Chem. Intelligent Lab. Syst., 136, 47, 10.1016/j.chemolab.2014.05.003 Wang, 2019, Incipient fault diagnosis of limit switch based on a ARMA model, Measurement, 135, 473, 10.1016/j.measurement.2018.11.080 Cerrada, 2015, Fault diagnosis in spur gears based on genetic algorithm and random forest, Mech. Syst. Signal Process., 70–71, 87 Quiroz, 2018, Fault detection of broken rotor bar in LS-PMSM using random forests, Measurement, 116, 273, 10.1016/j.measurement.2017.11.004 Suen, 1990, Recognition of totally un-constrained handwriting numerals based on the concept of multiple experts, Front. Hand-Writing Recog. Granger, 1989, Combining forecasts–twenty years later, J. Forecasting, 8, 167, 10.1002/for.3980080303 Barnett, 1981, Computational methods for a mathematical theory of evidence, Proc. IJCA, I, 868 Dasarathy, 1994 Liu, 2019, AEM: Attentional ensemble model for personalized classifier weight learning, Pattern Recogn., 96, 10.1016/j.patcog.2019.106976 Wang, 2018, Fault diagnosis for rotary machinery with selective ensemble neural networks, Mech. Syst. Signal Process., 113, 112, 10.1016/j.ymssp.2017.03.051 Ma, 2019, Ensemble deep learning-based fault diagnosis of rotor bearing systems, Comput. Ind., 105, 143, 10.1016/j.compind.2018.12.012 Zhou, 2002, Ensembling neural networks: many could be better than all, Artificial Intelligence, 137, 239, 10.1016/S0004-3702(02)00190-X Raschka, 2014 Ofir, 2019, Beyond majority: label ranking ensembles based on voting rules, Expert Syst. Appl., 136, 50, 10.1016/j.eswa.2019.06.022 Wang, 2012, A parameter optimization method for an SVM based on improved grid search algorithm, Appl. Sci. Technol. T. Bartzbeielstein. SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization. 73(4) (2010) 2222-2231. Chen, 2018, K-nearest neighbor algorithm optimization in text categorization, IOP Conf. Series Earth Environ. Sci., 108 Peterson, 2009, K-nearest neighbor, Scholarpedia, 4, 1883, 10.4249/scholarpedia.1883 Wylie, 2015, Enabling high-dimensional range queries using kNN indexing techniques: approaches and empirical results, J. Combinatorial Optimization, 32, 1 Leibe, 2006, Efficient clustering and matching for object class recognition, BMVC, 789 Adankon, 2002, Support vector machine, Comput. Sci., 1, 1 Burr Settles. Active Learning. Synthesis Lectures on Artificial Intelligence & Machine Learning, 332(6031) (2011) 765-765. H. He, F. Kong, J. Tan. DietCam: Multiview Food Recognition Using a Multikernel SVM. 20(3) (2017) 848-855. Singh, 2019, Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification, Inf. Med. Unlocked Breiman, 2001, Random Forests, Mach. Learning, 45, 5, 10.1023/A:1010933404324 Gashler, 2008, Decision tree ensemble: small heterogeneous is better than large homogeneous, 11 Canuto, 2007, Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles, Pattern Recogn. Lett., 28, 472, 10.1016/j.patrec.2006.09.001 Abdelali, 2019, Investigating the use of random forest in software effort estimation, Procedia Comput. Sci., 148, 343, 10.1016/j.procs.2019.01.042 Mitchell, 2011, Bias o Mustapha f the Random Forest Out-of-Bag (OOB) Error for Certain Input Parameters, Open J. Statistics, 01, 205, 10.4236/ojs.2011.13024 Comstock, 1999, Development of analysis tools for the evaluation of fault detection and diagnostics for chillers, ASHRAE Res. Project, 1043 Zhou, 2009, A novel strategy for the fault detection and diagnosis of centrifugal chiller systems, HVAC&R Res., 15, 57, 10.1080/10789669.2009.10390825 Cai, 2019, Classification complexity assessment for hyper-parameter optimization, Pattern Recogn. Lett., 125, 396, 10.1016/j.patrec.2019.05.021 Hsu, 2004 Han, 2011, Study on a hybrid SVM model for chiller FDD applications, Appl. Thermal Eng., 31, 582, 10.1016/j.applthermaleng.2010.10.021 Kohavi, 1995, A study of cross-validation and bootstrap for accuracy estimation and model selection, IJCAI, 14, 1137 Molinaro, 2005, Prediction error estimation: a comparison of resampling methods, Bioinformatics, 15, 15 Gou, 2012, A new distance-weighted k-nearest neighbor classifier, Inform. Comput. Sci., 9, 1429 Chen, 2019, Fast neighbor search by using revised k-d tree, Inf. Sci., 472, 145, 10.1016/j.ins.2018.09.012 Chang, 2015, Tuning of the hyper parameters for L2-Loss SVMs with the RBF kernel by the maximum-margin principle and the jackknife technique, Pattern Recogn., 48, 3983, 10.1016/j.patcog.2015.06.017 Bao, 2013, A PSO and pattern search based memetic algorithm for SVMs parameters optimization, Neurocomputing, 117, 98, 10.1016/j.neucom.2013.01.027 Shi, 2010 Liaw, 2002, Classification and Regression by randomForest, R News, 2, 18 Konrad, 2018, New diversity measure for data stream classification ensembles, Eng. Appl. Artif. Intelligence, 74, 23, 10.1016/j.engappai.2018.05.006 Tsymbal, 2005, Diversity in search strategies for ensemble feature selection, Inf. Fusion, 6, 83, 10.1016/j.inffus.2004.04.003 Kohavi, 1996, Bias plus variance decomposition for zero-one loss functions, 275 Kuncheva, 2003, Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy, Mach. Learning, 51, 181, 10.1023/A:1022859003006 Han, 2011, Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning, Int. J. Refrigeration, 34, 586, 10.1016/j.ijrefrig.2010.08.011 Pillai, 2012, F-measure optimisation in multi-label classifiers, Pattern Recogn. (ICPR) Adler, 2009, Bootstrap estimated true and false positive rates and ROC curve, Comput. Statistics Data Anal., 53, 718, 10.1016/j.csda.2008.09.023 Deng, 2016, An improved method to construct basic probability assignment based on the confusion matrix for classification problem, Inf. Sci., 340–341, 250, 10.1016/j.ins.2016.01.033 Xu, 2019, Three-way confusion matrix for classification: a measure driven view, Inf. Sci. Grace, 2005, Sensitivity of refrigeration system performance to charge levels and parameters for on-line leak detection, Appl. Thermal Eng., 25, 557, 10.1016/j.applthermaleng.2004.07.008 Friedman, 1937, The use of ranks to avoid the assumption of normality implicit in the analysis of variance, J. Am. Statistical Assoc., 32, 675, 10.1080/01621459.1937.10503522 Olmińska, 2016, Conversion of the helical tomotherapy plans to the step-and-shoot IMRT plans for patients with hip prosthesis during radiotherapy for prostate cancer, Phys. Med., 32, 260, 10.1016/j.ejmp.2015.11.007 Mitchell, 2000, To bonferroni or not to bonferroni: when and how are the questions, Bull. Ecol. Soc. Am., 81, 246 Cao, 2018, Preprocessing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning, IEEE Access, 1 Zhang, 2018, Integrating angle-frequency domain synchronous averaging technique with feature extraction for gear fault diagnosis, Mech. Syst. Signal Process., 99, 711, 10.1016/j.ymssp.2017.07.001 Zhao, 2013, Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD), Appl. Energy, 112, 1041, 10.1016/j.apenergy.2012.12.043 Azzeh, 2015, An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation, J. Syst. Software, 103, 36, 10.1016/j.jss.2015.01.028 Hosni, 2017, On the value of parameter tuning in heterogeneous ensembles effort estimation, Soft Comput. Kocaguneli, 2012, On the value of ensemble effort estimation, IEEE Trans. Software Eng., 38, 1403, 10.1109/TSE.2011.111 Mittas, 2013, Ranking and clustering software cost estimation models through a multiple comparisons algorithm, IEEE Trans. Software Eng., 39, 537, 10.1109/TSE.2012.45 Milicic, 2004, Distribution patterns of effort estimations// Euromicro Conference, IEEE, 422 Robson, Colin. Real World Research: A Resource for Social Scientists and Practitioner Researchers. Blackwell, 1993. Foody, 2020, Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification, Remote Sens. Environ., 239, 111630, 10.1016/j.rse.2019.111630