MLFS-CCDE: multi-objective large-scale feature selection by cooperative coevolutionary differential evolution

Feature selection is a pre-processing procedure of choosing the optimal feature subsets for constructing model, yet it is difficult to satisfy the requirements of reducing number of features and maintaining classification accuracy. Towards this problem, we propose novel multi-objectives large-scale cooperative coevolutionary algorithm for three-objectives feature selection, termed MLFS-CCDE. Firstly, a cooperative searching framework is designed for efficiently and effectively seeking for the optimal feature subset. Secondly, in the framework, three objectives, feature's number, classification accuracy and total information gain are established for guiding the evolution of features' combination. Thirdly, in framework's decomposition process, cluster-based decomposition strategy is elaborated for reducing the computation; in framework's coevolution process, dual indicator-based representatives are elaborated for balancing the representative solution' convergence and diversity. Finally, to verify framework's practicability, a heart disease diagnosis system based on MLFS-CCDE framework is constructed in cardiology. Numerical experiments demonstrate that the proposed MLFS-CCDE outperforms its competitors in terms of both classification accuracy and metrics of features' number.

Automated summary

The proposed novel multi-objectives large-scale cooperative coevolutionary algorithm, MLFS-CCDE, addresses the difficulty of feature selection by efficiently seeking for optimal feature subset and establishing three objectives for guiding features' evolution. The algorithm's practicality is verified through the construction of a heart disease diagnosis system, demonstrating superiority in classification accuracy and number of features compared to competitors.