Hybrid feature selection through feature clustering for microarray gene expression data

Authors:
Choudhury Muhammad Mufassil Wahid;A. B. M. Shawkat Ali;Kevin S. Tickle
Affiliations:
School of Information and Communication Technology, CQUniversity, Rockhampton, QLD, Australia;School of Information and Communication Technology, CQUniversity, Rockhampton, QLD, Australia;School of Information and Communication Technology, CQUniversity, Rockhampton, QLD, Australia
Venue:
International Journal of Hybrid Intelligent Systems
Year:
2013

Citing 13
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Feature selection for high-dimensional genomic microarray data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Chi2: Feature Selection and Discretization of Numeric Attributes

TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Minimum Redundancy Feature Selection from Microarray Gene Expression Data

CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
A novel feature selection method to improve classification of gene expression data

APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
On Feature Selection through Clustering

ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis

Expert Systems with Applications: An International Journal
An evolutionary method for combining different feature selection criteria in microarray data classification

Journal of Artificial Evolution and Applications - Special issue on artificial evolution methods in the biological and biomedical sciences
Feature selection for genomic data sets through feature clustering

International Journal of Data Mining and Bioinformatics
A novel hybrid feature selection method for microarray data analysis

Applied Soft Computing
A comparison of linear genetic programming and neural networks inmedical data mining

IEEE Transactions on Evolutionary Computation
Uniqueness of medical data mining

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Goal of feature selection is to find a suitable feature subset that produces higher accuracy for classifier in the user end. Hybrid methods for feature selection comprised of combination of filter and wrapper approaches have recently been emerged as strong techniques for the problem in this domain. In this paper we have presented a novel approach for feature selection based on feature clustering using well known k-means philosophy for the high dimensional gene expression data. Also we have proposed three simple hybrid approaches for reducing data dimensionality while maintaining classification accuracy which combine our basic feature selection through feature clustering FSFC approach to other standard approaches of feature selection in different orientation. We have employed popular Box and Whisker plot and ROC curve analysis to evaluate experimental outcome. Our experimental results clearly show suitability of our methods in hybrid approaches of feature selection in micro-array gene expression domain.