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Abstract
Feature selection is very crucial in the activities of soft computing algorithms for quality, precision and accuracy. This paper evaluates the performance of some feature selection methods of Genetic Algorithm in outlier detection on fingerprint images. Roulette wheel, Rank and Tournament methods were considered for feature selection and selected features were enhanced using histogram equalization. K-nearest neighbor algorithm was employed for classification to detect outliers. The implementation of the experiment was carried out in Matrix laboratory environment. Performance of the selection methods were evaluated based on metrics of accuracy, specificity, precision and computation time.
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Efunboade Ayodeji Oyeyinka, Adewale Josehp Adewale, Lawal Nurudeen Tunde A., and Sarumi Olusegun Ajibola (2020) “Performance Evaluation of Selection Methods of Genetic Algorithm in Outlier Detection “International Journal of Innovative Research and Advanced Studies (IJIRAS) Vol. 7 Issue 10 ISSN: 2394-4404.