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Multi-objective Filter-based Feature Selection Using NSGAIII With Mutual Information and Entropy

机译：具有互信息和熵的NSGAIII基于多目标过滤器的特征选择

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Feature selection (FS) aims to select the subsets of the most informative features by ignoring the redundant ones and consequently, improving the classification performance. Hence, consider as a two objective optimisation problem. Moreover, most of the existing work treats FS as single-objective by combining the two aims into a single fitness function. As such, there is a trade-off between the number of selected features and classification performance. To create a balance between the conflicting aim of the FS and yet improve classification performance, this study proposes the use of nondominated sorting genetic algorithm NSGAIII. Filter-based FS are scalable to large dimensional datasets and computationally fast. However, their classification performance is low because they lack feature interaction among the selected subset of features. Based on that mutual information (MI) along with entropy, are proposed as a filter-based evaluation measure along with the NSGAIII to have NSGAIIIMI and NSGAIIIE. The results obtained was compared with the existing single-objective, NSGAII as well as strength Pareto evolutionary algorithm with both MI and entropy. NSGAIII can successfully evolve the set of nondominated solutions and performs better in terms of the number of selected features, classification error rate and computational time on the majority of the datasets.
机译：特征选择（FS）旨在通过忽略冗余特征来选择信息最丰富的子集，从而提高分类性能。因此，将其视为两个目标优化问题。此外，大多数现有工作通过将两个目标组合为一个适应度函数来将FS视为单一目标。因此，在选定特征的数量和分类性能之间需要权衡。为了在FS的冲突目标之间取得平衡，同时又提高分类性能，本研究提出了使用非支配排序遗传算法NSGAIII的方法。基于过滤器的FS可扩展到大型数据集，并且计算速度快。但是，它们的分类性能很差，因为它们在所选特征子集之间缺乏特征交互作用。基于该信息和熵，与NSGAIII一起，提出了一种基于过滤器的评估措施，以使NSGAIIIMI和NSGAIIIE成为可能。将获得的结果与现有的单目标NSGAII以及具有MI和熵的强度Pareto进化算法进行了比较。 NSGAIII可以成功地演化出一组非支配解，并且在大多数数据集上，在选定特征的数量，分类错误率和计算时间方面表现更好。

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