An improved multi-label classification method and its application to functional genomics.
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.
Chen
Benhui
B
Graduate School of Information, Production and Systems, Waseda University, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japan. bhchen@fuji.waseda.jp
Gu
Weifeng
W
Hu
Jinglu
J
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D023281:Genomics / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated; D011506:Proteins / Q000145:classification
D011506:Proteins