文章基本信息

标题：Ensemble of Filter-Based Rankers to Guide an Epsilon-Greedy Swarm Optimizer for High-Dimensional Feature Subset Selection
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作者：Mohammad Bagher Dowlatshahi
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2017
卷号：8
期号：4
页码：152
DOI：10.3390/info8040152
语种：English
出版社：MDPI Publishing
摘要：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.
关键词：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