LAGAM: A Length-Adaptive Genetic Algorithm With Markov Blanket for High-Dimensional Feature Selection in Classification
Document Type
Article
Publication Date
11-1-2023
Abstract
Feature selection (FS) is an essential technique widely applied in data mining. Recent studies have shown that evolutionary computing (EC) is very promising for FS due to its powerful search capability. However, most existing EC-based FS methods use a length-fixed encoding to represent feature subsets. This inflexible encoding turns ineffective when high-dimension data are handled, because it results in a huge search space, as well as a large amount of training time and memory overhead. In this article, we propose a length-adaptive genetic algorithm with Markov blanket (LAGAM), which adopts a length-variable individual encoding and enables individuals to evolve in their own search space. In LAGAM, features are rearranged decreasingly based on their relevance, and an adaptive length changing operator is introduced, which extends or shortens an individual to guide it to explore in a better search space. Local search based on Markov blanket (MB) is embedded to further improve individuals. Experiments are conducted on 12 high-dimensional datasets and results reveal that LAGAM performs better than existing methods. Specifically, it achieves a higher classification accuracy by using fewer features.
Identifier
85142839938 (Scopus)
Publication Title
IEEE Transactions on Cybernetics
External Full Text Location
https://doi.org/10.1109/TCYB.2022.3163577
e-ISSN
21682275
ISSN
21682267
PubMed ID
36374903
First Page
6858
Last Page
6869
Issue
11
Volume
53
Grant
cstc2020jscx-gksbX0010
Fund Ref
Victoria University of Wellington
Recommended Citation
Zhou, Junhai; Wu, Quanwang; Zhou, Mengchu; Wen, Junhao; Al-Turki, Yusuf; and Abusorrah, Abdullah, "LAGAM: A Length-Adaptive Genetic Algorithm With Markov Blanket for High-Dimensional Feature Selection in Classification" (2023). Faculty Publications. 1359.
https://digitalcommons.njit.edu/fac_pubs/1359
