An efficient gene selection algorithm based on mutual information

Authors:
Ruichu Cai;Zhifeng Hao;Xiaowei Yang;Wen Wen
Affiliations:
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China;School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China;College of Mathematics Science, South China University of Technology, Guangzhou 510640, China;School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China
Venue:
Neurocomputing
Year:
2009

Citing 11
Cited 11

Elements of information theory

Elements of information theory
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Data mining: concepts and techniques

Data mining: concepts and techniques
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Input Feature Selection by Mutual Information Based on Parzen Window

IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection

The Journal of Machine Learning Research
Fast Binary Feature Selection with Conditional Mutual Information

The Journal of Machine Learning Research
LS Bound based gene selection for DNA microarray data

Bioinformatics
Random subspace method for multivariate feature selection

Pattern Recognition Letters
Input feature selection for classification problems

IEEE Transactions on Neural Networks
Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information

IEEE Transactions on Neural Networks

Boosting feature selection using information metric for classification

Neurocomputing
Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations

Neurocomputing
Efficiently mining local conserved clusters from gene expression data

Neurocomputing
A new combined filter-wrapper framework for gene subset selection with specialized genetic operators

MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Feature selection based on sensitivity analysis of fuzzy ISODATA

Neurocomputing
A modified two-stage SVM-RFE model for cancer classification using microarray data

ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Gene selection and PSO-BP classifier encoding a prior information

ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Feature evaluation and selection with cooperative game theory

Pattern Recognition
Using cooperative game theory to optimize the feature selection problem

Neurocomputing
Causal gene identification using combinatorial V-structure search

Neural Networks
A novel forward gene selection algorithm for microarray data

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Gene selection, a significant preprocessing of the discriminant analysis of microarray data, is to select the most informative genes from the whole gene set. In this paper, an efficient mutual information-based gene selection algorithm (MIGS) is proposed, in which genes are sequentially forward selected according to an approximate measure of the mutual information between the class and the selected genes. In order to improve the efficiency of the MIGS, an effective pruning strategy is introduced in the selection procedure as well as the employment of Parzen window density estimation technique. Extensive experiments are conducted on three public gene expression datasets and the experimental results confirm the efficiency and effectiveness of the algorithm. Though the computational cost of MIGS-Pruning increases with the number of selected genes, it still has good performance applied in the microarray problems.