Feature selection is an important pre-processing data mining task, which can reduce the data dimensionality and improve not only the classification accuracy but also the classifier efficiency. Filters use statistical characteristics of the data as the evaluation measure rather than using a classification algorithm. On the contrary, the wrapper process is computationally expensive because the evaluation of every feature subset requires running the classifier on the datasets and computing the accuracy from the obtained confusion matrix. In order to solve this problem, we propose a hybrid tri-objective evolutionary algorithm that optimizes two filter objectives, namely the number of features and the mutual information, and one wrapper objective corresponding to the accuracy. Once the population is classified into different non-dominated fronts, only feature subsets belonging to the first (best) one are improved using the indicator-based multi-objective local search. Our proposed hybrid algorithm, named Filter-Wrapper-based Nondominated Sorting Genetic Algorithm-II, is compared against several multi-objective and single-objective feature selection algorithms on eighteen benchmark datasets having different dimensionalities. Experimental results show that our proposed algorithm gives competitive and better results with respect to existing algorithms.

中文翻译：

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特征选择的多目标混合滤波器包装进化方法

特征选择是重要的预处理数据挖掘任务，它可以降低数据维数，不仅可以提高分类精度，而且可以提高分类器的效率。过滤器使用数据的统计特征作为评估指标，而不是使用分类算法。相反，包装过程在计算上是昂贵的，因为对每个特征子集的评估需要在数据集上运行分类器并从获得的混淆矩阵中计算准确性。为了解决这个问题，我们提出了一种混合三目标进化算法，该算法优化了两个滤波器目标，即特征数量和互信息，以及一个与精度相对应的包装目标。一旦人口被划分为不同的非主导阵线，使用基于指标的多目标局部搜索仅可改善属于第一个（最佳）的特征子集。在18种具有不同维数的基准数据集上，将我们提出的混合算法（基于过滤器包装器的非支配排序遗传算法II）与几种多目标和单目标特征选择算法进行了比较。实验结果表明，与现有算法相比，我们提出的算法具有更好的竞争性和更好的结果。在18个具有不同维数的基准数据集上，它与几种多目标和单目标特征选择算法进行了比较。实验结果表明，与现有算法相比，我们提出的算法具有更好的竞争性和更好的结果。在18个具有不同维数的基准数据集上，它与几种多目标和单目标特征选择算法进行了比较。实验结果表明，与现有算法相比，我们提出的算法具有更好的竞争性和更好的结果。