提出了两种新的特征选择算法。第一种是filter方法，第二种是wrapper方法。这两种算法都以多目标优化中的拥挤距离作为特征排序的度量。不太拥挤的特征对目标属性（类）有很大的影响。实验结果表明了算法的有效性和鲁棒性。

Two novel algorithms for features selection are proposed. The first one is a filter method while the second is wrapper method. Both the proposed algorithms 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.

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