crowded features selection

Version 1.0.0.2 (6.32 MB) by abdesslem layeb
Two novel features selection algorithms based on crowding distance
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Updated 12 May 2021
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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.

Cite As

abdesslem layeb (2024). crowded features selection (https://github.com/Layebuniv/crowdedfeatures/releases/tag/1.0.0.2), GitHub. Retrieved .

Abdesslem Layeb:Two novel feature selection algorithms based on crowding distance %https://arxiv.org/abs/2105.05212V3

MATLAB Release Compatibility
Created with R2021a
Compatible with any release
Platform Compatibility
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Version Published Release Notes
1.0.0.2

See release notes for this release on GitHub: https://github.com/Layebuniv/crowdedfeatures/releases/tag/1.0.0.2
To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.