A hybrid feature selection approach based on heuristic and exhaustive algorithms using Rough set theory

Muhammad Summair Raza, Usman Qamar
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引用次数: 13

Abstract

A dataset may have many irrelevant and unnecessary features, which not only increase computational space but also lead to a very critical phenomenon called curse of dimensionality. Feature selection process aims at selecting some relevant features for further processing on behalf of the entire dataset. However, to extract such information is non-trivial task, especially for large datasets. In literature many feature selection approaches have been proposed but recently rough set based heuristic approaches have become prominent ones. However, these approaches do not ensure the optimum solution. In this paper, a hybrid approach for feature selection has been proposed, based on heuristic algorithm and exhaustive search. Heuristic algorithm finds initial feature subset which is then further optimized by exhaustive search. We have used genetic algorithm and particle swarm optimization as preprocessor and relative dependency for optimization. Experiments have shown that our proposed approach is more effective and efficient as compared to the conventional relative dependency based approach.
基于粗糙集理论的启发式和穷举算法的混合特征选择方法
数据集可能有许多不相关和不必要的特征,这不仅增加了计算空间,而且还会导致一个非常关键的现象,即维数诅咒。特征选择过程的目的是代表整个数据集选择一些相关的特征进行进一步的处理。然而,提取这些信息是一项艰巨的任务,特别是对于大型数据集。在文献中提出了许多特征选择方法,但最近基于粗糙集的启发式方法成为突出的方法。然而,这些方法并不能保证最优解。本文提出了一种基于启发式算法和穷举搜索的混合特征选择方法。启发式算法首先找到初始特征子集,然后通过穷举搜索进一步优化。我们使用遗传算法和粒子群算法作为预处理和相对依赖来进行优化。实验表明,与传统的基于相对依赖的方法相比,我们提出的方法更加有效。
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