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An Evolutionary Approach for Balancing Effectiveness and Representation Level in Gene Selection

An Evolutionary Approach for Balancing Effectiveness and Representation Level in Gene Selection

Nicoletta Dessì, Barbara Pes, Laura Maria Cannas
Copyright: © 2015 |Volume: 8 |Issue: 2 |Pages: 18
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781466676503|DOI: 10.4018/jitr.2015040102
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MLA

Dessì, Nicoletta, et al. "An Evolutionary Approach for Balancing Effectiveness and Representation Level in Gene Selection." JITR vol.8, no.2 2015: pp.16-33. http://doi.org/10.4018/jitr.2015040102

APA

Dessì, N., Pes, B., & Cannas, L. M. (2015). An Evolutionary Approach for Balancing Effectiveness and Representation Level in Gene Selection. Journal of Information Technology Research (JITR), 8(2), 16-33. http://doi.org/10.4018/jitr.2015040102

Chicago

Dessì, Nicoletta, Barbara Pes, and Laura Maria Cannas. "An Evolutionary Approach for Balancing Effectiveness and Representation Level in Gene Selection," Journal of Information Technology Research (JITR) 8, no.2: 16-33. http://doi.org/10.4018/jitr.2015040102

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Abstract

As data mining develops and expands to new application areas, feature selection also reveals various aspects to be considered. This paper underlines two aspects that seem to categorize the large body of available feature selection algorithms: the effectiveness and the representation level. The effectiveness deals with selecting the minimum set of variables that maximize the accuracy of a classifier and the representation level concerns discovering how relevant the variables are for the domain of interest. For balancing the above aspects, the paper proposes an evolutionary framework for feature selection that expresses a hybrid method, organized in layers, each of them exploits a specific model of search strategy. Extensive experiments on gene selection from DNA-microarray datasets are presented and discussed. Results indicate that the framework compares well with different hybrid methods proposed in literature as it has the capability of finding well suited subsets of informative features while improving classification accuracy.

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