Feature selection based on multimodal multi-objective particle swarm optimization and prior information - Liu, Wenkai; Ling, Qinghua; Han, Fei; Han, Henry; Shi, Jinlong (Pattern analysis and applications : PAA, 2025)
Due to the conflicting objectives of classification accuracy and selected features size, feature selection is typically approached as a multi-objective optimization problem. However, traditional methods often overlook the inherent multimodal nature of feature selection. Additionally, these methods might ignore the importance of filter-based prior knowledge in forming equivalent feature subsets, weakening the ability to search for such subsets. An improved feature selection algorithm, named NRMOPSO, is proposed in this study, which is based on multimodal multi-objective particle swarm optimization and integrates a niche method with ReliefF. Initially, the Incrementally Expanding Niche Strategy (IENS) adjusts niche size for comprehensive initial exploration. Subsequently, the ReliefF algorithm evaluates feature importance, incorporating ReliefF-based prior information into the particle search to include significant unselected features while retaining essential ones. Experimental results on 14 UCI datasets indicate that the proposed algorithm effectively identifies multiple equivalent feature subsets and, on high-dimensional datasets, achieves smaller feature subsets without compromising classification accuracy when compared with five classical and advanced multimodal multi-objective optimization algorithms.