Feature selection for co-training  

作　　者：李国正 刘天羽 

机构地区：[1]School of Computer Engineering and Science, Shanghai University, Shanghai 200072, P. R. China [2]School of Electronic, Shanghai Dianji University, Shanghai 200240, P. R. China

出　　处：《Journal of Shanghai University(English Edition)》2008年第1期47-51,共5页上海大学学报（英文版）

基　　金：Project supported by the National Natural Science Foundation of China （Grant No.20503015）.

摘　　要：Co-training is a semi-supervised learning method, which employs two complementary learners to label the unlabeled data for each other and to predict the test sample together. Previous studies show that redundant information can help improve the ratio of prediction accuracy between semi-supervised learning methods and supervised learning methods. However, redundant information often practically hurts the performance of learning machines. This paper investigates what redundant features have effect on the semi-supervised learning methods, e.g. co-training, and how to remove the redundant features as well as the irrelevant features. Here, FESCOT （feature selection for co-training） is proposed to improve the generalization performance of co-training with feature selection. Experimental results on artificial and real world data sets show that FESCOT helps to remove irrelevant and redundant features that hurt the performance of the co-training method.Co-training is a semi-supervised learning method, which employs two complementary learners to label the unlabeled data for each other and to predict the test sample together. Previous studies show that redundant information can help improve the ratio of prediction accuracy between semi-supervised learning methods and supervised learning methods. However, redundant information often practically hurts the performance of learning machines. This paper investigates what redundant features have effect on the semi-supervised learning methods, e.g. co-training, and how to remove the redundant features as well as the irrelevant features. Here, FESCOT （feature selection for co-training） is proposed to improve the generalization performance of co-training with feature selection. Experimental results on artificial and real world data sets show that FESCOT helps to remove irrelevant and redundant features that hurt the performance of the co-training method.

关 键 词：feature selection semi-supervised learning CO-TRAINING 

分 类 号：TP30[自动化与计算机技术—计算机系统结构]