Informative Gene Selection for Microarray Classification via Adaptive Elastic Net with Conditional Mutual Information
Abstract
Due to the advantage of achieving a better performance under weak regularization, elastic net has attracted wide attention in statistics, machine learning, bioinformatics, and other fields. In particular, a variation of the elastic net, adaptive elastic net (AEN), integrates the adaptive grouping effect. In this paper, we aim to develop a new algorithm: Adaptive Elastic Net with Conditional Mutual Information (AEN-CMI) that further improves AEN by incorporating conditional mutual information into the gene selection process. We apply this new algorithm to screen significant genes for two kinds of cancers: colon cancer and leukemia. Compared with other algorithms including Support Vector Machine, Classic Elastic Net and Adaptive Elastic Net, the proposed algorithm, AEN-CMI, obtains the best classification performance using the least number of genes.
Keywords
Cite
@article{arxiv.1806.01466,
title = {Informative Gene Selection for Microarray Classification via Adaptive Elastic Net with Conditional Mutual Information},
author = {Xin-Guang Yang and Yongjin Lu},
journal= {arXiv preprint arXiv:1806.01466},
year = {2018}
}