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Multi-Class ROC Random Forest for Imbalanced Classification

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dc.contributor.advisor Zhu, Wei en_US
dc.contributor.advisor Wu, Song en_US
dc.contributor.author Yan, Jiaju en_US
dc.contributor.other Department of Applied Mathematics and Statistics en_US
dc.date.accessioned 2017-09-20T16:52:33Z
dc.date.available 2017-09-20T16:52:33Z
dc.date.issued 2017-05-01 en_US
dc.identifier.uri http://hdl.handle.net/11401/77355 en_US
dc.description 100 pg. en_US
dc.description.abstract The imbalanced class problem in classification is highly relevant in many realistic scenarios such as the detection of a rare condition. One solution is to design specific algorithms incorporating the unbalanced classes in the training process of a classifier. In this dissertation, we propose a novel multi-class classification tree based on the area under the ROC curve (AUC) to resolve the imbalanced classification problem. This tree classifier aims to maximize the sum of AUC for all one versus all classifiers at the node attribute selection stage while balancing the performance of sensitivity and specificity of all one versus all classification at the node threshold selection stage. The ROC tree is extended to ROC random forest with suitable modifications. Furthermore, the volume under surface (VUS), the extension of AUC for multi-class classification, is discussed in this dissertation as well and used to measure the performance of classifiers. The simulation results show that this multi-class ROC tree/forest method is superior to the classic CART/random forest on severely imbalanced multi-class classification problems, while the ROC random forest performs equally well as the SMOTE random forest on imbalanced binary classification problems. The application on Boston housing data shows that the ROC random forest can also be used for model ensemble and it performs better than all the base models and other ensemble methods in this application. en_US
dc.description.sponsorship This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. en_US
dc.format Monograph en_US
dc.format.medium Electronic Resource en_US
dc.language.iso en_US en_US
dc.publisher The Graduate School, Stony Brook University: Stony Brook, NY. en_US
dc.subject.lcsh Statistics en_US
dc.subject.other AUC, Imbalanced Classification, Model Ensemble, Random Forest, ROC, Tree Based Method en_US
dc.title Multi-Class ROC Random Forest for Imbalanced Classification en_US
dc.type Dissertation en_US
dc.mimetype Application/PDF en_US
dc.contributor.committeemember Kuan, Pei Fen en_US
dc.contributor.committeemember Xiao, Keli. en_US

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