MATEC Web Conf.
Volume 164, 2018The 3rd International Conference on Electrical Systems, Technology and Information (ICESTI 2017)
|Number of page(s)||10|
|Published online||23 April 2018|
Modified Floating Search Feature Selection Based on Genetic Algorithm
Graduate School of Applied Statistics National Institute of Development Administration, 118 Serithai Rd., Bangkapi, Bangkok, 10240, Thailand
Classification performance is adversely impacted by noisy data .Selecting features relevant to the problem is thus a critical step in classification and difficult to achieve accurate solution, especially when applied to a large data set. In this article, we propose a novel filter-based floating search technique for feature selection to select an optimal set of features for classification purposes. A genetic algorithm is utilized to increase the quality of features selected at each iteration. A criterion function is applied to choose relevant and high-quality features which can improve classification accuracy. The method is evaluated using 20 standard machine learning datasets of various sizes and complexities. Experimental results with the datasets show that the proposed method is effective and performs well in comparison with previously reported techniques.
Key words: Feature selection / Floating search / Genetic algorithm
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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