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Information 2017, 8(4), 152; doi:10.3390/info8040152

Ensemble of Filter-Based Rankers to Guide an Epsilon-Greedy Swarm Optimizer for High-Dimensional Feature Subset Selection

1
Department of Computer Engineering, Faculty of Engineering, Yazd University, Yazd P.O. Box 89195-741, Iran
2
Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman P.O. Box 76169-133, Iran
*
Author to whom correspondence should be addressed.
Received: 28 September 2017 / Revised: 19 October 2017 / Accepted: 20 November 2017 / Published: 22 November 2017
(This article belongs to the Special Issue Feature Selection for High-Dimensional Data)
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Abstract

The main purpose of feature subset selection is to remove irrelevant and redundant features from data, so that learning algorithms can be trained by a subset of relevant features. So far, many algorithms have been developed for the feature subset selection, and most of these algorithms suffer from two major problems in solving high-dimensional datasets: First, some of these algorithms search in a high-dimensional feature space without any domain knowledge about the feature importance. Second, most of these algorithms are originally designed for continuous optimization problems, but feature selection is a binary optimization problem. To overcome the mentioned weaknesses, we propose a novel hybrid filter-wrapper algorithm, called Ensemble of Filter-based Rankers to guide an Epsilon-greedy Swarm Optimizer (EFR-ESO), for solving high-dimensional feature subset selection. The Epsilon-greedy Swarm Optimizer (ESO) is a novel binary swarm intelligence algorithm introduced in this paper as a novel wrapper. In the proposed EFR-ESO, we extract the knowledge about the feature importance by the ensemble of filter-based rankers and then use this knowledge to weight the feature probabilities in the ESO. Experiments on 14 high-dimensional datasets indicate that the proposed algorithm has excellent performance in terms of both the error rate of the classification and minimizing the number of features. View Full-Text
Keywords: feature subset selection; hybrid filter-wrapper; high-dimensionality; Epsilon-greedy Swarm Optimizer; multi-objective optimization; swarm intelligence feature subset selection; hybrid filter-wrapper; high-dimensionality; Epsilon-greedy Swarm Optimizer; multi-objective optimization; swarm intelligence
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Dowlatshahi, M.B.; Derhami, V.; Nezamabadi-pour, H. Ensemble of Filter-Based Rankers to Guide an Epsilon-Greedy Swarm Optimizer for High-Dimensional Feature Subset Selection. Information 2017, 8, 152.

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