A New Feature Selection Method Based on a Self-Variant Genetic Algorithm Applied to Android Malware Detection
In solving classification problems in the field of machine learning and pattern recognition, the pre-processing of data is particularly important. The processing of high-dimensional feature datasets increases the time and space complexity of computer processing and reduces the accuracy of classifica...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/7/1290 |
| _version_ | 1797526006773317632 |
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| author | Le Wang Yuelin Gao Shanshan Gao Xin Yong |
| author_facet | Le Wang Yuelin Gao Shanshan Gao Xin Yong |
| author_sort | Le Wang |
| collection | DOAJ |
| description | In solving classification problems in the field of machine learning and pattern recognition, the pre-processing of data is particularly important. The processing of high-dimensional feature datasets increases the time and space complexity of computer processing and reduces the accuracy of classification models. Hence, the proposal of a good feature selection method is essential. This paper presents a new algorithm for solving feature selection, retaining the selection and mutation operators from traditional genetic algorithms. On the one hand, the global search capability of the algorithm is ensured by changing the population size, on the other hand, finding the optimal mutation probability for solving the feature selection problem based on different population sizes. During the iteration of the algorithm, the population size does not change, no matter how many transformations are made, and is the same as the initialized population size; this spatial invariance is physically defined as symmetry. The proposed method is compared with other algorithms and validated on different datasets. The experimental results show good performance of the algorithm, in addition to which we apply the algorithm to a practical Android software classification problem and the results also show the superiority of the algorithm. |
| first_indexed | 2024-03-10T09:21:06Z |
| format | Article |
| id | doaj.art-c8bfefd86d2340b1b016383930ff88b0 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T09:21:06Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-c8bfefd86d2340b1b016383930ff88b02023-11-22T05:10:08ZengMDPI AGSymmetry2073-89942021-07-01137129010.3390/sym13071290A New Feature Selection Method Based on a Self-Variant Genetic Algorithm Applied to Android Malware DetectionLe Wang0Yuelin Gao1Shanshan Gao2Xin Yong3School of Computer Science and Engineering, Northern Minzu University, Yinchuan 750021, ChinaNingxia Province Key Laboratory of Intelligent Information and Data Processing, North Minzu University, Yinchuan 750021, ChinaSchool of Computer Science and Engineering, Northern Minzu University, Yinchuan 750021, ChinaSchool of Computer Science and Engineering, Northern Minzu University, Yinchuan 750021, ChinaIn solving classification problems in the field of machine learning and pattern recognition, the pre-processing of data is particularly important. The processing of high-dimensional feature datasets increases the time and space complexity of computer processing and reduces the accuracy of classification models. Hence, the proposal of a good feature selection method is essential. This paper presents a new algorithm for solving feature selection, retaining the selection and mutation operators from traditional genetic algorithms. On the one hand, the global search capability of the algorithm is ensured by changing the population size, on the other hand, finding the optimal mutation probability for solving the feature selection problem based on different population sizes. During the iteration of the algorithm, the population size does not change, no matter how many transformations are made, and is the same as the initialized population size; this spatial invariance is physically defined as symmetry. The proposed method is compared with other algorithms and validated on different datasets. The experimental results show good performance of the algorithm, in addition to which we apply the algorithm to a practical Android software classification problem and the results also show the superiority of the algorithm.https://www.mdpi.com/2073-8994/13/7/1290feature selectionmachine learningasexualgenetic algorithmandroid malicious application detection |
| spellingShingle | Le Wang Yuelin Gao Shanshan Gao Xin Yong A New Feature Selection Method Based on a Self-Variant Genetic Algorithm Applied to Android Malware Detection Symmetry feature selection machine learning asexual genetic algorithm android malicious application detection |
| title | A New Feature Selection Method Based on a Self-Variant Genetic Algorithm Applied to Android Malware Detection |
| title_full | A New Feature Selection Method Based on a Self-Variant Genetic Algorithm Applied to Android Malware Detection |
| title_fullStr | A New Feature Selection Method Based on a Self-Variant Genetic Algorithm Applied to Android Malware Detection |
| title_full_unstemmed | A New Feature Selection Method Based on a Self-Variant Genetic Algorithm Applied to Android Malware Detection |
| title_short | A New Feature Selection Method Based on a Self-Variant Genetic Algorithm Applied to Android Malware Detection |
| title_sort | new feature selection method based on a self variant genetic algorithm applied to android malware detection |
| topic | feature selection machine learning asexual genetic algorithm android malicious application detection |
| url | https://www.mdpi.com/2073-8994/13/7/1290 |
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