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Genetic Diagnosis of Cancer by Evolutionary Fuzzy-Rough based Neural-Network Ensemble

Genetic Diagnosis of Cancer by Evolutionary Fuzzy-Rough based Neural-Network Ensemble

Sujata Dash, Bichitrananda Patra
Copyright: © 2016 |Volume: 6 |Issue: 1 |Pages: 16
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781466691360|DOI: 10.4018/IJKDB.2016010101
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MLA

Dash, Sujata, and Bichitrananda Patra. "Genetic Diagnosis of Cancer by Evolutionary Fuzzy-Rough based Neural-Network Ensemble." IJKDB vol.6, no.1 2016: pp.1-16. http://doi.org/10.4018/IJKDB.2016010101

APA

Dash, S. & Patra, B. (2016). Genetic Diagnosis of Cancer by Evolutionary Fuzzy-Rough based Neural-Network Ensemble. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 6(1), 1-16. http://doi.org/10.4018/IJKDB.2016010101

Chicago

Dash, Sujata, and Bichitrananda Patra. "Genetic Diagnosis of Cancer by Evolutionary Fuzzy-Rough based Neural-Network Ensemble," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 6, no.1: 1-16. http://doi.org/10.4018/IJKDB.2016010101

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Abstract

High dimension and small sample size is an inherent problem of gene expression datasets which makes the analysis process more complex. The present study has developed a novel learning scheme that encapsulates a hybrid evolutionary fuzzy-rough feature selection model with an adaptive neural net ensemble. Fuzzy-rough method deals with uncertainty and impreciseness of real valued gene expression dataset and evolutionary search concept optimizes the subset selection process. The efficiency of the hybrid-FRGSNN model is evaluated by the proposed neural net ensemble learning algorithm. Again to prove the learning capability of ensemble algorithm, performance of the component classifiers pairing with FR, GSNN and FRGSNN are compared with proposed hybrid-FRGSNN based ensemble model. In addition to this, efficiency of neural net ensemble is compared with two classical and one advanced ensemble learning algorithms.

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