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A GA-like Dynamic Probability Method With Mutual Information for Feature Selection by Wang, Gaoshuai; Lauri, Fabrice; Hassani, Amir Hajjam El is a scholarly article available to read on EtoBox.

What is A GA-like Dynamic Probability Method With Mutual Information for Feature Selection about?

Feature selection plays a vital role in promoting the classifier's performance. However, current methods ineffectively distinguish the complex interaction in the selected features. To further remove these hidden negative interactions, we propose a GA-like dynamic probability (GADP) method with mutual information which has a two-layer structure. The first layer applies the mutual information method to obtain a primary feature subset. The GA-like dynamic probability algorithm, as the second layer, mines more supportive features based on the former candidate features. Essentially, the GA-like method is one of the population-based algorithms so its work mechanism is similar to the GA. Different from the popular works which frequently focus on improving GA's operators for enhancing the search ability and lowering the converge time, we boldly abandon GA's operators and employ the dynamic probability that relies on the performance of each chromosome to determine feature selection in the new generation. The dynamic probability mechanism significantly reduces the parameter number in GA that making it easy to use. As each gene's probability is independent, the chromosome variety in GADP is m

Author

Wang, Gaoshuai; Lauri, Fabrice; Hassani, Amir Hajjam El

Published

2022

Language

EN