Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10361003 | Pattern Recognition | 2011 | 10 Pages |
Abstract
In this paper, we propose a new BAyesian Semi-SUpervised Method, or BASSUM in short, to exploit the values of unlabelled samples on classification feature selection problem. Generally speaking, the inclusion of unlabelled samples helps the feature selection algorithm on (1) pinpointing more specific conditional independence tests involving fewer variable features and (2) improving the robustness of individual conditional independence tests with additional statistical information. Our experimental results show that BASSUM enhances the efficiency of traditional feature selection methods and overcomes the difficulties on redundant features in existing semi-supervised solutions.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Ruichu Cai, Zhenjie Zhang, Zhifeng Hao,