In this paper, two novel algorithms for features selection are proposed. The first one is a filter method while the second is wrapper method. Both the proposed algorithm use the crowding distance used in the multiobjective optimization as a metric in order to sort the features. The less crowded features have great effects on the target attribute (class). The experimental results have shown the effectiveness and the robustness of the proposed algorithms.

中文翻译：

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两种基于拥挤距离的新颖特征选择算法

本文提出了两种新颖的特征选择算法。第一个是过滤方法，第二个是包装方法。两种提出的算法都使用多目标优化中使用的拥挤距离作为度量，以对特征进行排序。较不拥挤的功能对目标属性（类）有很大的影响。实验结果表明了所提算法的有效性和鲁棒性。