Bio-inspired optimization for feature set dimensionality reduction

Citation:
Elharir, E., N. El-Bendary, and A. E. Hassanien, "Bio-inspired optimization for feature set dimensionality reduction", 3rd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA),, Beirut, Lebanon, 13-15 July , 2016.

Date Presented:

13-15 July

Abstract:

In this paper, two novel bio-inspired optimization algorithms; namely Dragonfly Algorithm (DA) and Grey Wolf Optimizer (GWO), have been applied for fulfilling the goal of feature set dimensional reduction. The proposed classification system has been tested via solving the problem of Electromyography (EMG) signal classification with optimal features subset selection. The obtained experimental results showed that the GWO based Support Vector Machines (SVM) classification algorithm has achieved an accuracy of 93.22% using 31% of the total extracted features. It also outperformed both the typical SVM algorithm, with no feature set optimization, and the DA based optimized feature set SVM classification, for the tested EMG dataset.

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