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A Multi-objective Feature Selection Approach Based on Binary PSO and Rough Set Theory

机译：基于二元PSO和粗糙集理论的多目标特征选择方法

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摘要

Feature selection has two main objectives of maximising the classification performance and minimising the number of features. However, most existing feature selection algorithms are single objective wrapper approaches. In this work, we propose a multi-objective filter feature selection algorithm based on binary particle swarm optimisation (PSO) and probabilistic rough set theory. The proposed algorithm is compared with other five feature selection methods, including three PSO based single objective methods and two traditional methods. Three classification algorithms (na?ve bayes, decision trees and k-nearest neighbours) are used to test the generality of the proposed filter algorithm. Experiments have been conducted on six datasets of varying difficulty. Experimental results show that the proposed algorithm can automatically evolve a set of non-dominated feature subsets. In almost all cases, the proposed algorithm outperforms the other five algorithms in terms of both the number of features and the classification performance (evaluated by all the three classification algorithms). This paper presents the first study on using PSO and rough set theory for multi-objective feature selection.
机译：特征选择具有最大化分类性能并最大限度地减少功能数量的两个主要目标。但是，大多数现有特征选择算法是单个目标包装器方法。在这项工作中，我们提出了一种基于二元粒子群优化（PSO）和概率粗糙集理论的多目标滤波器特征选择算法。将所提出的算法与其他五种特征选择方法进行比较，包括三种基于PSO的单个物镜方法和两种传统方法。三种分类算法（Na？ve Bayes，决策树和k最近邻居）用于测试所提出的滤波器算法的一般性。在不同困难的六个数据集上进行了实验。实验结果表明，该算法可以自动地发展一组非主导的特征子集。在几乎所有情况下，所提出的算法在特征数量和分类性能（由所有三种分类算法评估）方面优于其他五种算法。本文介绍了使用PSO和粗糙集理论进行多目标特征选择的第一次研究。

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