Two novel feature selection algorithms based on crowding distance
This work addresses feature selection for data analysis, but it appears incremental as it builds on existing crowding distance concepts.
The paper tackled feature selection by proposing two algorithms using crowding distance from multiobjective optimization to sort features, and experimental results demonstrated their effectiveness and robustness.
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.