The fuzzy gene filter: an adaptive fuzzy inference system for expression array feature selection

Authors:
Meir Perez;David M. Rubin;Tshilidzi Marwala;Lesley E. Scott;Jonathan Featherston;Wendy Stevens
Affiliations:
Department of Electrical and Electronic Engineering Technology, University of Johannesburg, South Africa;School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa;Department of Electrical and Electronic Engineering Technology, University of Johannesburg, South Africa;Department of Molecular Medicine and Haematology, University of the Witwatersrand, Johannesburg, South Africa;The National Health Laboratory Service, Johannesburg, South Africa;The National Health Laboratory Service, Johannesburg, South Africa
Venue:
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Year:
2010

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Abstract

The identification of class differentiating genes is central to microarray data classification. Genes are ranked in order of differential expression and the optimal top ranking genes are selected as features for classification. In this paper, a new approach to gene ranking, based on a fuzzy inference system - the Fuzzy Gene Filter - is presented and compared to classical ranking approaches (the t-test, Wilcoxon test and ROC analysis). Two performance metrics are used; maximum Separability Index and highest cross-validation accuracy. The techniques were implemented on two publically available data-sets. The Fuzzy Gene Filter outperformed the other techniques both with regards to maximum Separability Index, as well as highest cross-validation accuracy. For the prostate data-set it a attained a Leave-one-out cross-validation accuracy of 96.1% and for the lymphoma data-set, 100%. The Fuzzy Gene Filter cross-validation accuracies were also higher than those recorded in previous publications which used the same data-sets. The Fuzzy Gene Filter's success is ascribed to its incorporation of both parametric and non-parametric data features and its ability to be optimised to suit the specific data-set under analysis.