Feature Selection Using Multi Swarm Optimization Empowered by Classification in Medical Dataset
6 Pages Posted: 19 Mar 2018
Date Written: November 15, 2017
Feature selection is a technique by which the number of features in the dataset is reduced to a minimal amount to do the classification in effective manner. Without feature selection classification uses all the attributes which increases the time complexity and decreases efficiency. In the medical dataset there are large number of attributes to classify the diseases so the feature selection can be used here to classify the data efficiently. The multi swarm optimization technique is used for the feature selection process and wrap it up with the classification algorithm and propose a hybrid algorithm for the effective classification process. MSO yields same or better effectiveness (solution quality) than other optimization techniques such as Genetic Algorithms, and Ant Colony Optimization by using less number of function evaluations.This paper, focus towards the mechanism of using multi-swarm optimization empowered by classification in medical dataset to improve the performance and efficiency, measures such as accuracy and precision rate.
Keywords: Datamining, Feature Selection, Classification, Swarm Optimization
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