A Review of Grey Wolf Optimizer-Based Feature Selection Methods for Classification

Al-Tashi, Qasem; Rais, Helmi Md; Abdulkadir, Said Jadid; Mirjalili, Seyedali; Alhussian, Hitham

doi:10.1007/978-981-32-9990-0_13

Cited by 128 publications

(76 citation statements)

References 22 publications

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“…[42], [43]. Our recent published book chapter [14] reviews all the approaches of feature selection based GWO published during the period of 2015 up to 2019. Although, recent studies have used multi-objective optimizations to solve the problem of feature selection.…”

Section: Related Workmentioning

confidence: 99%

“…GWO is a recently proposed metaheuristic technique influenced by the natural social intelligence of the grey wolves. The GWO has fewer parameters which makes it cheap in terms of computational cost and faster convergence [14]. In fact, feature selection is a minimization issue with two goals: (1) minimizing the error rate of classification (maximizing the performance of classification) and (2) minimizing number of features.…”

Section: Introductionmentioning

confidence: 99%

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Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

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Feature selection or dimensionally reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multiobjective problem, most existing approaches treat feature selection as a single-objective optimization problem. Recently, Multiobjective Grey Wolf optimizer (MOGWO) was proposed to solve multi-objective optimization problem. However, MOGWO was originally designed for continuous optimization problems and hence, it cannot be utilized directly to solve multi-objective feature selection problems which are inherently discrete in nature. Therefore, in this research, a binary version of MOGWO based on sigmoid transfer function called BMOGW-S is developed to optimize feature selection problems. A wrapper based Artificial Neural Network (ANN) is used to assess the classification performance of a subset of selected features. To validate the performance of the proposed method, 15 standard benchmark datasets from the UCI repository are employed. The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO). The results showed that the proposed BMOGWO-S can effectively determine a set of non-dominated solutions. The proposed method outperforms the existing multi-objective approaches in most cases in terms of features reduction as well as classification error rate while benefiting from a lower computational cost.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

et al. 2020

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“…Al-Tashi et al [9] proposed a binary version of the hybrid particle swarm optimization (PSO) and grey wolf optimization (GWO) to fix the problems of feature selection. There are many improvements in GWO for feature selection issues such as [29]- [31]. There are some drawbacks to the current grey wolf optimization algorithm, such as low precision and moderate convergence rate.…”

Section: Related Workmentioning

confidence: 99%

Improved Feature Selection Model for Big Data Analytics

et al. 2020

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Although there are many attempts to build an optimal model for feature selection in Big Data applications, the complex nature of processing such kind of data makes it still a big challenge. Accordingly, the data mining process may be obstructed due to the high dimensionality and complexity of huge data sets. For the most informative features and classification accuracy optimization, the feature selection process constitutes a mandatory pre-processing phase to reduce dataset dimensionality. The exhaustive search for the relevant features is time-consuming. In this paper, a new binary variant of the wrapper feature selection grey wolf optimization and particle swarm optimization is proposed. The K-nearest neighbor classifier with Euclidean separation matrices is used to find the optimal solutions. A tent chaotic map helps in avoiding the algorithm from locked to the local optima problem. The sigmoid function employed for converting the search space from a continuous vector to a binary one to be suitable to the problem of feature selection. Crossvalidation K-fold is used to overcome the overfitting issue. A variety of comparisons have been made with well-known and common algorithms, such as the particle swarm optimization algorithm, and the grey wolf optimization algorithm. Twenty datasets are used for the experiments, and statistical analyses are conducted to approve the performance and the effectiveness and of the proposed model with measures like selected features ratio, classification accuracy, and computation time. The cumulative features picked through the twenty datasets were 196 out of 773, as opposed to 393 and 336 in the GWO and the PSO, respectively. The overall accuracy is 90% relative to other algorithms ' 81.6 and 86.8. The total processing time for all datasets equals 184.3 seconds, wherein GWO and PSO equal 272 and 245.6, respectively. INDEX TERMS Particle swarm optimization (PSO), grey wolf optimization (GWO), data mining, big data analytics, feature selection.

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“…Swarm intelligence algorithms are efficient heuristic search methods for the wrapper-based feature selection problems [11]. For example, several classical swarm intelligence algorithms such as genetic algorithm (GA) [12], ant colony optimization (ACO) [13], differential evolution (DE) [14], grey wolf optimization (GWO) [15] and dragon algorithm (DA) [16].…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Feature Selection: An Improved Binary Particle Swarm Optimization Approach

Sun

et al. 2020

IEEE Access

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Feature selection is an effective approach to reduce the number of features of data, which enhances the performance of classification in machine learning. In this paper, we formulate a joint feature selection problem to reduce the number of the selected features while enhancing the accuracy. An improved binary particle swarm optimization (IBPSO) algorithm is proposed to solve the formulated problem. IBPSO introduces a local search factor based on Lévy flight, a global search factor based on weighting inertia coefficient, a population diversity improvement factor based on mutation mechanism and a binary mechanism to improve the performance of conventional PSO and to make it suitable for the binary feature selection problems. Experiments based on 16 classical datasets are selected to test the effectiveness of the proposed IBPSO algorithm, and the results demonstrate that IBPSO has better performance than some other comparison algorithms.

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A Review of Grey Wolf Optimizer-Based Feature Selection Methods for Classification

Cited by 128 publications

References 22 publications

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

Improved Feature Selection Model for Big Data Analytics

Bio-Inspired Feature Selection: An Improved Binary Particle Swarm Optimization Approach

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