EMaTO-LFS: Evolutionary Many-Task Optimization-Based Localized Feature Selection for High-Dimensional Classification

Localized feature selection (LFS) partitions the sample space into multiple local regions and selects superior feature subsets per region. However, existing LFS methods treat each region separately when selecting features, ignoring the overlaps and correlations among neighboring regions. This approach limits classification performance in high-dimensional (HD) scenarios. To address this limitation, we model LFS as a many-task optimization problem, where each local region is treated as a separate multiobjective feature selection (FS) task while considering regional correlations. Generally, we propose a novel evolutionary many-task optimization-based LFS (EMaTO-LFS) framework (EMaTO-LFS), which enables collaborative solving of HD FS tasks by sharing knowledge among correlated regions. Specifically, we use a method that utilizes the ratio of positive and negative samples to adaptively construct local regions, while a filter-based method prefilters feature subsets for each local task in advance. We also design a subset-based mutation operator, improving the method's ability to search for superior feature combinations. In addition, a strategy for transferring knowledge between local regions is formulated based on neighborhood relationships, effectively avoiding negative transfer due to conflicting tasks. Empirical results on 14 HD datasets show that evolutionary many-task optimization (EmaTO)-LFS achieves competitive balanced accuracy with smaller feature subsets than state-of-the-art FS and LFS methods.