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A hybrid genetic algorithm for feature subset selection in rough set theory

机译：粗糙集理论中用于特征子集选择的混合遗传算法

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Rough set theory has been proven to be an effective tool to feature subset selection. Current research usually employ hill-climbing as search strategy to select feature subset. However, they are inadequate to find the optimal feature subset since no heuristic can guarantee optimality. Due to this, many researchers study stochastic methods. Since previous works of combination of genetic algorithm and rough set theory do not show competitive performance compared with some other stochastic methods, we propose a hybrid genetic algorithm for feature subset selection in this paper, called HGARSTAR. Different from previous works, HGARSTAR embeds a novel local search operation based on rough set theory to fine-tune the search. This aims to enhance GA's intensification ability. Moreover, all candidates (i.e. feature subsets) generated in evolutionary process are enforced to include core features to accelerate convergence. To verify the proposed algorithm, experiments are performed on some standard UCI datasets. Experimental results demonstrate the efficiency of our algorithm.
机译：粗糙集理论已被证明是特征子集选择的有效工具。当前的研究通常采用爬山作为搜索策略来选择特征子集。然而，由于没有启发式方法可以保证最优性，因此它们不足以找到最优特征子集。因此，许多研究人员研究了随机方法。由于以前的遗传算法和粗糙集理论相结合的工作与其他一些随机方法相比并没有表现出竞争性能，因此本文提出了一种用于特征子集选择的混合遗传算法，称为HGARSTAR。与以前的作品不同，HGARSTAR嵌入了一种基于粗糙集理论的新颖本地搜索操作，以对搜索进行微调。目的在于增强通用航空的强化能力。此外，强制将在进化过程中生成的所有候选（即特征子集）包括核心特征以加速收敛。为了验证提出的算法，对一些标准的UCI数据集进行了实验。实验结果证明了该算法的有效性。

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