Feature selection with multi-objective genetic algorithm based on a hybrid filter and the symmetrical complementary coefficient

With the expansion of data size and data dimension, feature selection attracts more and more attention. In this paper, we propose a novel feature selection algorithm, namely, Hybrid filter and Symmetrical Complementary Coefficient based Multi-Objective Genetic Algorithm feature selection (HSMOGA). HSMOGA contains a new hybrid filter, Symmetrical Complementary Coefficient which is a well-performed metric of feature interactions proposed recently, and a novel way to limit feature subset's size. A new Pareto-based ranking function is proposed when solving multi-objective problems. Besides, HSMOGA starts with a novel step called knowledge reserve, which precalculate the knowledge required for fitness function calculation and initial population generation. In this way, HSMOGA is classifier-independent in each generation, and its initial population generation makes full use of the knowledge of data set which makes solutions converge faster. Compared with other GA-based feature selection methods, HSMOGA has a much lower time complexity. According to experimental results, HSMOGA outperforms other nine state-of-art feature selection algorithms including five classic and four more recent algorithms in terms of kappa coefficient, accuracy, and G-mean for the data sets tested.

Automated summary

HSMOGA is a novel feature selection algorithm that introduces a hybrid filter, Symmetrical Complementary Coefficient, and a new method to limit feature subset size. It uses a Pareto-based ranking function to solve multi-objective problems and precalculates knowledge using a step called knowledge reserve, leading to faster convergence of solutions. Experimental results show that HSMOGA outperforms other nine feature selection algorithms in terms of performance metrics such as kappa coefficient, accuracy, and G-mean.