Two novel features selection algorithms based on crowding distance

• URL: http://arxiv.org/abs/2105.05212v2
• Date: Thu, 13 May 2021 15:19:50 GMT
• Title: Two novel features selection algorithms based on crowding distance
• Authors: Abdesslem Layeb
• Abstract summary: The proposed algorithms use the crowding distance used in the multiobjective optimization as a metric to sort the features. The experimental results have shown the effectiveness and the robustness of the proposed algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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