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 (FS) is an important data processing technique in the field of machine learning. There have been various FS methods, but all assume that the cost associated with a feature is precise, which restricts their real applications. Focusing on the FS problem with fuzzy cost, a fuzzy multiobjective FS method with particle swarm optimization, called PSOMOFS, is studied in this article. The proposed method develops a fuzzy dominance relationship to compare the goodness of candidate particles and defines a fuzzy crowding distance measure to prune the elitist archive and determine the global leader of particles. Also, a tolerance coefficient is introduced into the proposed method to ensure that the Pareto-optimal solutions obtained satisfy decision makers’ preferences. The developed method is used to tackle a series of the UCI datasets and is compared with three fuzzy multiobjective evolutionary methods and three typical multiobjective FS methods. Experimental results show that the proposed method can achieve feature sets with superior performances in approximation, diversity, and feature cost.

Multiobjective Particle Swarm Optimization for Feature Selection With Fuzzy Cost

The invention discloses a multi-objective particle group optimization feature selection method suitable for a fuzzy representation of feature quality The steps are as follows: (1) the problem is modeled to a two-objective optimization problem including fuzzy information, classifier evaluation precision is a precise objective function, and feature subset reliability is a fuzzy objective function; (2) an appropriate coding strategy is adopted, the abovementioned feature selection problem with a discrete variable is converted to a multi-objective optimization problem with a continuous variable; and (3) an improved multi-objective particle group optimization algorithm is used to perform global optimization on the two objective functions determined in the step 2, thereby obtaining a Pareto optimal set of the problem; a fuzzy probability dominance relation and a decision maker tolerance coefficient are defined, and an external reserving set of the algorithm is updated The multi-objective particle group optimization feature selection method in the invention gives full play to the advantages of fast convergence rate, few adjustable parameters, and simple and easily realized rules of the particle group optimization algorithm, is used for handling the feature selection problem whose performance index is a fuzzy number, application examples prove that the method in the invention can obtain a solution richer and more valid than that obtained by a conventional method, thus, the method in the invention provides new thinking capable of being used for reference for handling the feature selection problem with a fuzzy representation of data quality

Ying Hu

Papers

Feature selection algorithm based on bare bones particle swarm optimization

Multiobjective Particle Swarm Optimization for Feature Selection With Fuzzy Cost

Multi-objective particle group feature selection method under fuzzy representation of data quality