Unsupervised feature selection using a neuro-fuzzy approach
Pattern Recognition Letters
Neuro-fuzzy feature evaluation with theoretical analysis
Neural Networks
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
LS Bound based gene selection for DNA microarray data
Bioinformatics
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
Neuro-fuzzy methodology for selecting genes mediating lung cancer
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Unsupervised feature evaluation: a neuro-fuzzy approach
IEEE Transactions on Neural Networks
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In this article, we propose a methodology for selecting genes that may have a role in mediating a disease in general and certain cancers in particular. The methodology, first of all, groups an entire set of genes. Then the important group is determined using two neuro-fuzzy models. Finally, individual genes from the most important group are evaluated in terms of their importance in mediating a cancer, and important genes are selected. A method for multiplying existing data is also proposed to create a data rich environment in which neuro-fuzzy models are effective. The effectiveness of the proposed methodology is demonstrated using five microarray gene expression data sets dealing with human lung, colon, sarcoma, breast and leukemia. Moreover, we have made an extensive comparative analysis with 22 existing methods using biochemical pathways, p-value, t-test, F-test, sensitivity, expression profile plots, pi-GSEA, Fisher-score, KOGS, SPEC, W-test and BWS, for identifying biologically and statistically relevant gene sets. It has been found that the proposed methodology has been able to select genes that are more biologically significant in mediating certain cancers than those obtained by the others.