Feature Selection Using an Improved Gravitational Search Algorithm

Zhu, Lei; He, Shoushuai; Wang, Lei; Zeng, Weijun; Yang, Jian

doi:10.1109/access.2019.2935833

Cited by 20 publications

(13 citation statements)

References 30 publications

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“…Furthermore, Zhu et al [ 290 ] proposed an improved GSA known as IGSA, which adopted the concept of global memory and the definition of exponential Kbest to improve the baseline GSA. In this approach, the authors improved the exploitation ability of the IGSA by memorizing the optimal solution obtained, thereby preventing the particles from premature convergence and slow movement, maintaining an equilibrium between the exploration and exploitation.…”

Section: Metaheuristic Algorithms For Multiclass Feature Selectionmentioning

confidence: 99%

Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

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Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.

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Section: Metaheuristic Algorithms For Multiclass Feature Selectionmentioning

confidence: 99%

Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Here, G(t) is the gravitational constant, and its formulation is defined as follows where T is the total number of iterations, G 0 represents the initial value for the gravitational constant and α is a coefficient. 24…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…where T is the total number of iterations, G 0 represents the initial value for the gravitational constant and is a coefficient. 24 The resultant force for agent X i in the dth dimension is the randomly weighted sum of forces from Kbest agents. The Kbest denotes the set of the first K agents with the best fitness values.…”

Section: Gravitational Search Algorithmmentioning

confidence: 99%

An enhanced quantum-inspired gravitational search algorithm for color prediction based on the absorption spectrum

Gao

Zhang

Zhou

et al. 2020

Textile Research Journal

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Spot color is widely applied to printing and packing in modern industry, which can satisfy the individualization requirements and express the emotion of products. Color prediction is the core technique for spot color restoration. In this paper, a method that combines the least squares method and gravitation search algorithm is proposed to address the color prediction by using the absorption spectrum. Firstly, the spectral transmittance of the thin film with high transmission and low reflectance characteristics is researched to find the absorbance. Secondly, the least squares method is used to ascertain the primary colors of the spot color. Thirdly, an enhanced quantum gravitation search algorithm is designed to predict the spot color. The predicted results on the 30 spot colors show that the proposed method has higher accuracy in comparison with the three existed methods. The color differences between the prepared spot colors and the reproduced spot colors are all less than 3, in which 75% of the color differences are less 1 and 35% of the color differences are less 0.1. All the results confirm that the proposed method can predict the spot color accurately.

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“…Mistry et al [25] propose a micro-GA embedded PSO feature selection method for the intelligent facial emotion recognition problems. Zhu et al [26] propose an improved gravitational search algorithm to solve the feature selection problems, and the effectiveness of the proposed method are evaluated on several widely used datasets. Taradeh et al [27] propose another gravitational search-based algorithm for feature selection.…”

Section: Related Workmentioning

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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Feature Selection Using an Improved Gravitational Search Algorithm

Cited by 20 publications

References 30 publications

Multiclass feature selection with metaheuristic optimization algorithms: a review

Multiclass feature selection with metaheuristic optimization algorithms: a review

An enhanced quantum-inspired gravitational search algorithm for color prediction based on the absorption spectrum

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

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