Iterative extreme learning machine for single class classifier using general mapping convergence framework

Authors:
Nguyen Ha Vo;Minh-Tuan T. Hoang;Hieu T. Huynh;Jung-Ja Kim;Yonggwan Won
Affiliations:
Department of Computer Engineering, Chonnam National University, Kwangju, Republic of Korea;Department of Computer Engineering, Chonnam National University, Kwangju, Republic of Korea;Department of Computer Engineering, Chonnam National University, Kwangju, Republic of Korea;Division of Bionics and Bioinformatics, Chonbuk National University, Chonbuk, Republic of Korea;Department of Computer Engineering, Chonnam National University, Kwangju, Republic of Korea
Venue:
ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
Year:
2006

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Abstract

Single Class Classification (SCC) is the problem to distinguish one class of data (called positive class) from the rest data of multiple classes (negative class). SCC problems are common in real world where positive and unlabeled data are available but negative data is expensive or very hard to acquire. In this paper, extreme leaning machine (ELM), a recently developed machine learning algorithm, is fused with mapping convergence algorithm that is based on the support vector machine (SVM). The proposed method achieves both high accuracy in classification, very fast learning and high speed in operation.