The Meta-heuristic Binary Shuffled Frog Leaping and Genetic Algorithms in Selecting Efficient Significant Features

Volume 13, Issue 2, pp 130 - 135 http://dx.doi.org/10.22436/jmcs.013.02.04

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Authors

Saeed Ayat - Associate Professor, Department of Computer Engineering and Information Technology, Payame Noor University, Iran. Mohammad Reza Mohammadi Khoroushani - M.Sc. student, Department of Computer Engineering and Information Technology, Payame Noor University, Esfahan, Iran.

Abstract

Selecting the most suitable features among a collection of features to achieve accuracy, sensitivity and efficiency is considered as a big challenge in pattern recognition systems. In this study, the two binary genetic and the binary shuffled frog leaping evolutionary algorithms are evaluated with respect to efficient feature selection in a medical detecting system. The results point to the effectiveness of selection of the most suitable features through memetic Meta heuristic binary frog leaping in increasing the accuracy, sensitivity in detection and time saving in the Classification process against the genetic algorithm.

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Keywords

• feature selection
• genetic
• Meta heuristic
• shuffled frog leaping
• skin lesions

MSC

•  97D50
•  92Dxx

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