Open AccessA New Embedded Feature Selection Method using IBALO mixed with MRMR criteria
Author(s) -
Yu Zhang,
Zhuanzhe Zhao,
Shanshan Zhao,
Yongming Liu,
Kang He
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1453/1/012027
Subject(s) - feature selection , redundancy (engineering) , minimum redundancy feature selection , computer science , pattern recognition (psychology) , selection (genetic algorithm) , artificial intelligence , binary number , ant colony optimization algorithms , feature (linguistics) , mathematics , linguistics , philosophy , arithmetic , operating system
In order to remove irrelevant data and increase classification accuracy infeature selection, this paper proposed a new Embedded feature selection methodwith gathering Minimal Redundancy Maximal Relevance (MRMR) criteria, SequentialForward Selection (SFS) and Improved Binary Ant Lion Optimizer (IBALO) together.Totally, we use three different feature selection methods namely MRMR mixed withSequential Forward Selection (MS), MS mixed with Binary Ant Lion Optimizer(MS-BALO), an improvement of MS-BALO. Experiments prove that the MS-IBALO methodproposed by this paper is efficient compared to MS and MS-BALO methods.