Fuzzy feature evaluation index and connectionist realization
Information Sciences: an International Journal
Unsupervised feature selection using a neuro-fuzzy approach
Pattern Recognition Letters
Fuzzy feature evaluation index and connectionist realization — II: theorectical analysis
Information Sciences—Informatics and Computer Science: An International Journal
Neuro-fuzzy feature evaluation with theoretical analysis
Neural Networks
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
Unsupervised feature evaluation: a neuro-fuzzy approach
IEEE Transactions on Neural Networks
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
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In this article, we describe neuro-fuzzy models under supervised and unsupervised learning for selecting a few possible genes mediating a disease. The methodology involves grouping of genes based on correlation coefficient using microarray gene expression patterns. The most important group is selected using existing neuro-fuzzy systems [1,2,3,4,5]. Finally, a few possible genes are selected from the most important group using the aforesaid neuro-fuzzy systems. The effectiveness of the methodology has been demonstrated on lung cancer gene expression data sets. The superiority of the methodology has been established with four existing gene selection methods like SAM, SNR, NA and BR. The enrichment of each gene ontology category of the resulting genes was calculated by its P-value. The genes output the low P-value, and indicate that they are biologically significant. According to the methodology, we have found more true positive genes than the other existing algorithms.