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Open AccessArticle

The Cross-Entropy Based Multi-Filter Ensemble Method for Gene Selection

1
School of Information Science and Engineering, Ningbo University, Ningbo 315000, China
2
College of Engineering, Lishui University, Lishui 323000, China
*
Author to whom correspondence should be addressed.
Genes 2018, 9(5), 258; https://doi.org/10.3390/genes9050258
Received: 12 March 2018 / Revised: 20 April 2018 / Accepted: 2 May 2018 / Published: 17 May 2018
The gene expression profile has the characteristics of a high dimension, low sample, and continuous type, and it is a great challenge to use gene expression profile data for the classification of tumor samples. This paper proposes a cross-entropy based multi-filter ensemble (CEMFE) method for microarray data classification. Firstly, multiple filters are used to select the microarray data in order to obtain a plurality of the pre-selected feature subsets with a different classification ability. The top N genes with the highest rank of each subset are integrated so as to form a new data set. Secondly, the cross-entropy algorithm is used to remove the redundant data in the data set. Finally, the wrapper method, which is based on forward feature selection, is used to select the best feature subset. The experimental results show that the proposed method is more efficient than other gene selection methods and that it can achieve a higher classification accuracy under fewer characteristic genes. View Full-Text
Keywords: cross-entropy; multi-filter; gene expression profile; ensemble method; gene selection cross-entropy; multi-filter; gene expression profile; ensemble method; gene selection
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MDPI and ACS Style

Sun, Y.; Lu, C.; Li, X. The Cross-Entropy Based Multi-Filter Ensemble Method for Gene Selection. Genes 2018, 9, 258.

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