A Feature Selection Method of the Island Algorithm Based on Gaussian Mutation. (12th March 2022)

• Record Type: Journal Article
• Authors: Han, Li; Xu, Hongsheng; Ma, Jiming; Jia, Zechen
• Other Names: Rajakani Kalidoss Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-03-12
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/1438999
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : With the development of the Internet, the data we are dealing with is becoming more and more complex, which brings about various difficulties and problems when we process and use data. Feature selection methods are able to filter and remove redundant features, which is necessary to reduce the dimension of complex data. In this paper, the island algorithm is used to find the optimal feature subset in the set of feature subsets, but as the number of iterations increasing, the island algorithm tends to local optimization. To address this problem, a Gaussian mutation strategy is introduced to improve the island algorithm and proposed an island algorithm based on Gaussian mutation (IAGM). The main idea of the IAGM algorithm is to set a warning sign which aims to record the global optimum value generated by each iteration and judge whether the changes are less than a threshold value in the optimum value for three consecutive iterations. If it is less than the global optimum, the Gaussian mutation is applied to the current globally optimal plant location and then evaluates the new location and exchanges them when it is better than the global optimum. Otherwise, discard this plant and add a Gaussian mutation to the formula of the next new plant towards the global optimum, which is used to diversify the populations. Meanwhile, combining the IAGM algorithm with a support vector machine classifier, a feature selection method of the island algorithm based on Gaussian mutation (IAGMFS) is proposed. The UCI dataset is selected for simulation experiments and applied to classify images, which represents the advantages of the feature selection in IAGMFS proposed in this paper.