Feature Selection Based on a Large‐Scale Many‐Objective Evolutionary Algorithm

Abstract

The feature selection problem is a fundamental issue in many research fields. In this paper, the feature selection problem is regarded as an optimization problem and addressed by utilizing a large-scale many-objective evolutionary algorithm. Considering the number of selected features, accuracy, relevance, redundancy, interclass distance, and intraclass distance, a large-scale many-objective feature selection model is constructed. It is difficult to optimize the large-scale many-objective feature selection optimization problem by using the traditional evolutionary algorithms. Therefore, this paper proposes a modified vector angle-based large-scale many-objective evolutionary algorithm (MALSMEA). The proposed algorithm uses polynomial mutation based on variable grouping instead of naive polynomial mutation to improve the efficiency of solving large-scale problems. And a novel worst-case solution replacement strategy using shift-based density estimation is used to replace the poor solution of two individuals with similar search directions to enhance convergence. The experimental results show that MALSMEA is competitive and can effectively optimize the proposed model.

Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications

“…(f) Miscellaneous: In [75], the authors use six different metrics to build the objective function, defined in Section 4.2.1 and named as follows:…”

“…The reference point usually is the anti-optimal point or "worst possible" point in the objective space. This metric has been used in [62][63][64][65][67][68][69][70]73,75,120,179] (d) Inverted generational distance (IGD): Computes the average Euclidean distance from true Pareto fronts to its closest solution in the population. This metric has been used in [64,65,68,73,75] and mathematically is defined as follows:…”

“…The authors report the average number of features selected in the performed runs and their standard deviation. This metric has been used in [21,27,28,32,34,[36][37][38]40,42,[44][45][46][47][48][49][50][51][53][54][55]57,58,[63][64][65][66][67][68][69][70][71][72]75,77,78,[81][82][83][84][85][86][87][88][89][90][91][92][93][94][95]…”

“…(f) Relevance: Represents the relevance between features and categorical variables and reflects the recognition ability of the selected features. This metric has been used in [75].…”

“…(g) Redundancy: Quantifies the level of similarity between selected features. This metric has been used in [75].…”