A Projected Feature Selection Algorithm for Data Classification

摘要：

In contrast to many popular feature selection algorithms that provide suboptimal solutions according to some criterion, the OCFS algorithm can ensure optimal solutions according to the orthogonal centroid criterion. Based on the properties of OCFS, this paper proposes a projected feature selection algorithm called projected OCFS (POCFS) for data classification. POCFS extends OCFS to select different features for each class pair individually rather than to select the same features for all the classes simultaneously. Thus, It can select more suitable features for classifier construction than OCFS. Experimental results on real data set KDDCUP99 indicate that POCFS outperforms OCFS in terms of their effectiveness and efficiency.

关键词： feature selection classification OCFS

作者: Zhiwu Yin Shangteng Huang

作者单位: Dept. of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, 200240, China

会议类型: 国际会议

会议名称: 第三届IEEE无线通讯、网络技术暨移动计算国际会议

会议地点: 上海

会议语种:英文

在线出版日期: 2007-09-21（万方平台首次上网日期，不代表论文的发表时间）