Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.

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A Survey on Evolutionary Computation Approaches to Feature Selection

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Investigating the impact of data normalization on classification performance

Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features This kind of problem is called cost-based feature selection However, most existing feature selection approaches treat this task as a single-objective optimization problem This paper presents the first study of multi-objective particle swarm optimization PSO for cost-based feature selection problems The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems

Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification

This paper proposes a facial expression recognition system using evolutionary particle swarm optimization (PSO)-based feature optimization. The system first employs modified local binary patterns, which conduct horizontal and vertical neighborhood pixel comparison, to generate a discriminative initial facial representation. Then, a PSO variant embedded with the concept of a micro genetic algorithm (mGA), called mGA-embedded PSO, is proposed to perform feature optimization. It incorporates a nonreplaceable memory, a small-population secondary swarm, a new velocity updating strategy, a subdimension-based in-depth local facial feature search, and a cooperation of local exploitation and global exploration search mechanism to mitigate the premature convergence problem of conventional PSO. Multiple classifiers are used for recognizing seven facial expressions. Based on a comprehensive study using within- and cross-domain images from the extended Cohn Kanade and MMI benchmark databases, respectively, the empirical results indicate that our proposed system outperforms other state-of-the-art PSO variants, conventional PSO, classical GA, and other related facial expression recognition models reported in the literature by a significant margin.

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A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition

Feature selection algorithm based on bare bones particle swarm optimization

Feature selection is an important preprocessing technique for data. Brain storm optimization (BSO) is one of the latest swarm intelligence algorithms, which simulates the collective behavior of human beings. However, traditional updating mechanisms in BSO limit its application in feature selection. We study a new individual clustering technology and two individual updating mechanisms in BSO for developing novel feature selection algorithms with the purpose of maximizing the classification performance. The proposed individual updating mechanisms are compared with each other. The more promising updating mechanism and the new individual clustering technology are combined into the BSO framework to form a new wrapper feature selection algorithm, called BBSOFS. Compared with existing algorithms including particle swarm optimization, firefly algorithm and BSO algorithm, experimental results on benchmark datasets show that with the help of the proposed individual clustering and updating mechanism, the proposed BBSOFS algorithm can obtain feature subsets with good classification accuracy.

Brain storm optimization for feature selection using new individual clustering and updating mechanism

With the increase of the number of features and the sample size, existing feature selection (FS) methods based on evolutionary optimization still face challenges such as the “curse of dimensionality” and the high computational cost. In view of this, dividing or clustering the sample and feature spaces at the same time, this article proposes a hybrid FS algorithm using surrogate sample-assisted particle swarm optimization (SS-PSO). First, a nonrepetitive uniform sampling strategy is employed to divide the whole sample set into several small-size sample subsets. Regarding each sample subset as a surrogate unit, next, a collaborative feature clustering mechanism is proposed to divide the feature space, with the purpose of reducing both the computational cost of clustering feature and the search space of PSO. Following that, an ensemble surrogate-assisted integer PSO is proposed. To ensure the prediction accuracy of ensemble surrogate when evaluating particles, an ensemble surrogate construction and management strategy is designed. Since the whole sample set is replaced by a small number of surrogate units, SS-PSO significantly reduces the cost of evaluating particles in PSO. Finally, the proposed algorithm is applied to some typical datasets, and compared with six typical evolutionary FS algorithms, as well as its several variant algorithms. The experimental results show that SS-PSO can obtain good feature subsets at the smallest computational cost on most of datasets. All verify that SS-PSO is a highly competitive method for high-dimensional FS.

Surrogate Sample-Assisted Particle Swarm Optimization for Feature Selection on High-Dimensional Data

Odor source localization is a challenging task for mobile robots in the real world. A refined hybrid particle swarm optimization by combining with bacterial foraging optimization is proposed for odor source localization of a swarm of robots. In the proposed algorithm, a chemotaxis operation is integrated into particle swarm optimization to lead robots to track a plume, and an elimination-dispersal operation is adopted to prevent particles trapping in local minim. Simulation results show that the proposed method can find an odor source with higher success rate.

Wanqiu Zhang

Papers

Feature selection algorithm based on bare bones particle swarm optimization

Brain storm optimization for feature selection using new individual clustering and updating mechanism

Surrogate Sample-Assisted Particle Swarm Optimization for Feature Selection on High-Dimensional Data

Localizing odor source with multi-robot based on hybrid particle swarm optimization

An embedded vertical‐federated feature selection algorithm based on particle swarm optimisation