Performance Evaluation of Ranking Methods for Relevant Gene Selection in Cancer Microarray Datasets
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- 作者：Manju Sardana (21)
Baljeet Kaur (22)
R. K. Agrawal (21) - 关键词：Microarrays ; Ranking method ; Brown Forsythe test ; Gini Index ; Mutual Information ; Pearson Coefficient ; Cochran test ; Adjusted Welch test
- 刊名：Lecture Notes in Computer Science
- 出版年：2013
- 出版时间：2013
- 年：2013
- 卷：7629
- 期：1
- 页码：419-431
- 全文大小：292KB
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Baljeet Kaur (22)
R. K. Agrawal (21)
21. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India, 110067
22. Hansraj College, Delhi University, Delhi, India, 110007 - ISSN：1611-3349
文摘
Microarray data is often characterized by high dimension and small sample size. Gene ranking is one of the most widely explored techniques to reduce the dimension because of its simplicity and computational efficiency. Many ranking methods have been suggested which depict their efficiency dependent upon the problem at hand. We have investigated the performance of six ranking methods on eleven cancer microarray datasets. The performance is evaluated in terms of classification accuracy and number of genes. Experimental results on all dataset show that there is significant variation in classification accuracy which depends on the choice of ranking method and classifier. Empirical results show that Brown Forsythe test statistics and Mutual Information method exhibit high accuracy with few genes whereas Gini Index and Pearson Coefficient perform poorly in most cases.