Title: An improved multi-label classification method and its application to functional genomics

Authors: Benhui Chen, Weifeng Gu, Jinglu Hu

Addresses: Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japan. ' Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japan. ' Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japan

Abstract: In this paper, a multi-label classification method based on label ranking and delicate boundary Support Vector Machine (SVM) is proposed for solving the functional genomics applications. Firstly, an improved probabilistic SVM with delicate decision boundary is used as scoring approach to obtain a proper label rank. Secondly, an instance-dependent thresholding strategy is proposed to decide classification results. A d-folds validation approach is utilised to determine a set of target thresholds for all training samples as teachers, then an appropriate instance-dependent threshold for each testing instance is obtained by applying k-Nearest Neighbours (KNN) strategy on this teacher threshold set.

Keywords: multi-label classification; ranking based methods; SVM; support vector machines; thresholding strategy; functional genomics; label ranking.

DOI: 10.1504/IJCBDD.2010.035239

International Journal of Computational Biology and Drug Design, 2010 Vol.3 No.2, pp.133 - 145

Available online: 16 Sep 2010 *

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