SVM-Boosting based on Markov resampling: Theory and algorithm

In this article we introduce the idea of Markov resampling for Boosting methods. We first prove that Boosting algorithm with general convex loss function based on uniformly ergodic Markov chain (u.e.M.c.) examples is consistent and establish its fast convergence rate. We apply Boosting algorithm bas...

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Vydáno v:Neural networks Ročník 131; s. 276 - 290
Hlavní autoři: Jiang, Hongwei, Zou, Bin, Xu, Chen, Xu, Jie, Tang, Yuan Yan
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Ltd 01.11.2020
Témata:
Boosting
Consistency
Markov Chains
Resampling
Support Vector Machine - standards
Uniformly ergodic Markov chain (u.e.M.c.)
Boosting
Resampling
Consistency
Uniformly ergodic Markov chain (u.e.M.c.)
ISSN:0893-6080, 1879-2782, 1879-2782
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Shrnutí:In this article we introduce the idea of Markov resampling for Boosting methods. We first prove that Boosting algorithm with general convex loss function based on uniformly ergodic Markov chain (u.e.M.c.) examples is consistent and establish its fast convergence rate. We apply Boosting algorithm based on Markov resampling to Support Vector Machine (SVM), and introduce two new resampling-based Boosting algorithms: SVM-Boosting based on Markov resampling (SVM-BM) and improved SVM-Boosting based on Markov resampling (ISVM-BM). In contrast with SVM-BM, ISVM-BM uses the support vectors to calculate the weights of base classifiers. The numerical studies based on benchmark datasets show that the proposed two resampling-based SVM Boosting algorithms for linear base classifiers have smaller misclassification rates, less total time of sampling and training compared to three classical AdaBoost algorithms: Gentle AdaBoost, Real AdaBoost, Modest AdaBoost. In addition, we compare the proposed SVM-BM algorithm with the widely used and efficient gradient Boosting algorithm-XGBoost (eXtreme Gradient Boosting), SVM-AdaBoost and present some useful discussions on the technical parameters.
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2020.07.036