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

Genes
Yingqiang SunXiaobo Li

Abstract

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.

References

Jun 29, 2004·Artificial Intelligence in Medicine·Iñaki InzaAntonio J Cerrolaza
Oct 14, 2005·IEEE Transactions on Nanobioscience·Kai-Bo DuanFrancisco Azuaje
Aug 28, 2007·Bioinformatics·Yvan SaeysPedro Larrañaga
Feb 13, 2010·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Yukyee Leung, Yeungsam Hung
Apr 29, 2010·International Journal of Data Mining and Bioinformatics·Huilin XiongJiangsheng Yu
Feb 25, 2014·Computers in Biology and Medicine·Wengang Zhou, Julie A Dickerson
Dec 17, 2014·Journal of Biomedical Informatics·Iman KamkarSvetha Venkatesh
Feb 26, 2015·Computers in Biology and Medicine·Somsak RakkeitwinaiApiwat Mutirangura

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Citations

Dec 11, 2019·Scientific Reports·Jamshid PirgaziMohammad Hossein Olyaee

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Methods Mentioned

BETA
chips

Software Mentioned

weka
mRMR
Windows
CEMFE
matlab

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