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Multi-class classification via heterogeneous ensemble of one-class classifiers

Authors
Kang, SeokhoCho, SungzoonRang, Pilsung
Issue Date
8월-2015
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Multi-class classification; One-class classification; Meta-learning; Stacking; Ensemble; Heterogeneous ensemble
Citation
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v.43, pp.35 - 43
Indexed
SCIE
SCOPUS
Journal Title
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume
43
Start Page
35
End Page
43
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/92902
DOI
10.1016/j.engappai.2015.04.003
ISSN
0952-1976
Abstract
In this paper, a multi-class classification method based on heterogeneous ensemble of one-class classifiers is proposed. The proposed method consists of two phases: training heterogeneous one-class classifiers for each class using various one-class classification algorithms, and constructing an ensemble by combining the base classifiers using multi-response linear regression-based stacking. The use of various classification algorithms contributes towards increasing the diversity of the ensemble, while stacking resolves the normalization issues on different scales of outputs obtained from the base classifiers. In addition, we also demonstrate the selective utilization of base classifiers by adopting a stepwise variable selection procedure during stacking. Through our experiments on multi-class benchmark datasets, we concluded that our proposed method outperforms the methods that are based on single one-class classification algorithms with statistical significance. (C) 2015 Elsevier Ltd. All rights reserved.
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공과대학 (산업경영공학부)
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