Multi-objective Evolutionary Algorithms for filter Based Feature Selection in Classification
Multi-objective Evolutionary Algorithms for filter Based Feature Selection in Classification
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DOI：
10.1142/s0218213013500243
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发表时间：
2013-08
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影响因子：
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通讯作者：
Bing Xue;Liam Cervante;L. Shang;Will N. Browne;Mengjie Zhang
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文献类型：
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作者：
Bing Xue;Liam Cervante;L. Shang;Will N. Browne;Mengjie Zhang
特征选择是一个多目标问题，有两个主要的相互冲突的目标，最小化的特征数量和最大化的分类性能。然而，现有的特征选择算法大多是单目标的，不能很好地反映实际需要。存在少量的多目标特征选择算法，其是基于包装器的，并且因此在计算上昂贵并且不如过滤器算法通用。进化计算技术特别适合于多目标优化，因为它们使用候选解的种群，并且能够在单次运行中找到多个非支配解。然而，两个著名的进化多目标算法，基于非支配排序的多目标遗传算法II（NSGAII）和强度Pareto进化算法2（SPEA2）没有被应用到基于过滤器的特征选择。在这项工作中，基于NSGAII和SPEA2，我们开发了两个多目标，基于过滤器的特征选择框架。四个多目标的特征选择方法，然后开发应用互信息和熵作为两个不同的过滤器评价标准，在每一个提出的框架。提出的多目标算法进行了检查，并与一个单目标的方法和三个传统的方法（两个过滤器和一个包装器）在八个基准数据集进行比较。一个决策树被用来测试分类性能。实验结果表明，所提出的多目标算法可以自动进化出一组非支配的解决方案，其中包括一个较小的数量的功能，并取得更好的分类性能比使用所有功能。NSGAII和SPEA2在大多数情况下，无论是特征数量还是分类性能都优于单目标算法、两种传统的过滤算法，甚至优于传统的包装算法。对于由少量特征组成的数据集，NSGAII实现了与SPEA2相似的性能，并且当特征数量较大时，结果略好。这项工作是第一次研究NSGAII和SPEA2的过滤器特征选择的分类问题，都提供领域领先的分类性能。
Feature selection is a multi-objective problem with the two main conflicting objectives of minimising the number of features and maximising the classification performance. However, most existing feature selection algorithms are single objective and do not appropriately reflect the actual need. There are a small number of multi-objective feature selection algorithms, which are wrapper based and accordingly are computationally expensive and less general than filter algorithms. Evolutionary computation techniques are particularly suitable for multi-objective optimisation because they use a population of candidate solutions and are able to find multiple non-dominated solutions in a single run. However, the two well-known evolutionary multi-objective algorithms, nondominated sorting based multi-objective genetic algorithm II (NSGAII) and strength Pareto evolutionary algorithm 2 (SPEA2) have not been applied to filter based feature selection. In this work, based on NSGAII and SPEA2, we develop two multi-objective, filter based feature selection frameworks. Four multi-objective feature selection methods are then developed by applying mutual information and entropy as two different filter evaluation criteria in each of the two proposed frameworks. The proposed multi-objective algorithms are examined and compared with a single objective method and three traditional methods (two filters and one wrapper) on eight benchmark datasets. A decision tree is employed to test the classification performance. Experimental results show that the proposed multi-objective algorithms can automatically evolve a set of non-dominated solutions that include a smaller number of features and achieve better classification performance than using all features. NSGAII and SPEA2 outperform the single objective algorithm, the two traditional filter algorithms and even the traditional wrapper algorithm in terms of both the number of features and the classification performance in most cases. NSGAII achieves similar performance to SPEA2 for the datasets that consist of a small number of features and slightly better results when the number of features is large. This work represents the first study on NSGAII and SPEA2 for filter feature selection in classification problems with both providing field leading classification performance.