Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines
A hybrid GA & back propagation approach for gene selection and classification of microarray data
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
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
Neuro-fuzzy methodology for selecting genes mediating lung cancer
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Comparison of feature selection methods in ECG signal classification
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
A hybrid GA/SVM approach for gene selection and classification of microarray data
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Hybrid feature selection through feature clustering for microarray gene expression data
International Journal of Hybrid Intelligent Systems
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This paper introduces a novel method for minimum number of gene (feature) selection for a classification problem based on gene expression data with an objective function to maximise the classification accuracy. The method uses a hybrid of Pearson correlation coefficient (PCC) and signal-to-noise ratio (SNR) methods combined with an evolving classification function (ECF). First, the correlation coefficients between genes in a set of thousands, is calculated. Genes, that are highly correlated across samples are considered either dependent or co-regulated and form a group (a cluster). Signal-to-noise ratio (SNR) method is applied to rank the correlated genes in this group according to their discriminative power towards the classes. Genes with the highest SNR are used in a preliminary feature set as representatives of each group.An incremental algorithm that consists of selecting a minimum number of genes (variables) from the preliminary feature set, starting from one gene, is then applied for building an optimum classification system. Only variables, that increase the classification rate in each of the validation iteration, are selected and added to the final feature set. The results show that the proposed hybrid PCC, SNR and ECF method improves the feature selection process in terms of number of variables required and also improves the classification rate. The classification accuracy of the ECF classifier is tested through the leave one out method for validation.