  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 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.
