Ant Colony Optimisation Classification for Gene Expression Data Analysis

Authors:
Gerald Schaefer
Affiliations:
Department of Computer Science, Loughborough University, Loughborough, U.K.
Venue:
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Year:
2009

Citing 6
Cited 1

Ant Colony Optimization

Ant Colony Optimization
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

Bioinformatics
Classification using partial least squares with penalized logistic regression

Bioinformatics
cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes

ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Data mining of gene expression data by fuzzy and hybrid fuzzy methods

IEEE Transactions on Information Technology in Biomedicine
Data mining with an ant colony optimization algorithm

IEEE Transactions on Evolutionary Computation

An adaptive discretization in the ACDT algorithm for continuous attributes

ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Microarray studies and gene expression analysis have received much attention over the last few years and provide promising avenues towards the understanding of fundamental questions in biology and medicine. In this paper we investigate the application of ant colony optimisation (ACO) based classification for the analysis of gene expression data. We employ cAnt-Miner, a variation of the classical Ant-Miner classifier, to interpret numerical gene expression data. Experimental results on well-known gene expression datasets show that the ant-based approach is capable of extracting a compact rule base and provides good classification performance.