Feature selection based on fuzzy joint mutual information maximization
Nowadays, real-world applications handle a huge amount of data, especially with high-dimension features space. These datasets are a significant challenge for classification systems. Unfortunately, most of the features present are irrelevant or redundant, thus making these systems inefficient and ina...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2021-04-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | http://www.aimspress.com/article/doi/10.3934/mbe.2021016?viewType=HTML |
| _version_ | 1818945586254053376 |
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| author | Omar A. M. Salem Feng Liu Ahmed Sobhy Sherif Wen Zhang Xi Chen |
| author_facet | Omar A. M. Salem Feng Liu Ahmed Sobhy Sherif Wen Zhang Xi Chen |
| author_sort | Omar A. M. Salem |
| collection | DOAJ |
| description | Nowadays, real-world applications handle a huge amount of data, especially with high-dimension features space. These datasets are a significant challenge for classification systems. Unfortunately, most of the features present are irrelevant or redundant, thus making these systems inefficient and inaccurate. For this reason, many feature selection (FS) methods based on information theory have been introduced to improve the classification performance. However, the current methods have some limitations such as dealing with continuous features, estimating the redundancy relations, and considering the outer-class information. To overcome these limitations, this paper presents a new FS method, called Fuzzy Joint Mutual Information Maximization (FJMIM). The effectiveness of our proposed method is verified by conducting an experimental comparison with nine of conventional and state-of-the-art feature selection methods. Based on 13 benchmark datasets, experimental results confirm that our proposed method leads to promising improvement in classification performance and feature selection stability. |
| first_indexed | 2024-12-20T08:01:28Z |
| format | Article |
| id | doaj.art-c1111d2f1c1146f1819ba74f0aac8a7c |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-20T08:01:28Z |
| publishDate | 2021-04-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-c1111d2f1c1146f1819ba74f0aac8a7c2022-12-21T19:47:30ZengAIMS PressMathematical Biosciences and Engineering1551-00182021-04-0118130532710.3934/mbe.2021016Feature selection based on fuzzy joint mutual information maximizationOmar A. M. Salem0Feng Liu1Ahmed Sobhy Sherif2Wen Zhang3Xi Chen41. School of Computer Science, Wuhan University, Wuhan 430072, China 2. Faculty of Computers and Informatics, Suez Canal University, Ismailia 41522, Egypt1. School of Computer Science, Wuhan University, Wuhan 430072, China2. Faculty of Computers and Informatics, Suez Canal University, Ismailia 41522, Egypt3. College of informatics, Huazhong Agricultural University, Wuhan 430070, China1. School of Computer Science, Wuhan University, Wuhan 430072, ChinaNowadays, real-world applications handle a huge amount of data, especially with high-dimension features space. These datasets are a significant challenge for classification systems. Unfortunately, most of the features present are irrelevant or redundant, thus making these systems inefficient and inaccurate. For this reason, many feature selection (FS) methods based on information theory have been introduced to improve the classification performance. However, the current methods have some limitations such as dealing with continuous features, estimating the redundancy relations, and considering the outer-class information. To overcome these limitations, this paper presents a new FS method, called Fuzzy Joint Mutual Information Maximization (FJMIM). The effectiveness of our proposed method is verified by conducting an experimental comparison with nine of conventional and state-of-the-art feature selection methods. Based on 13 benchmark datasets, experimental results confirm that our proposed method leads to promising improvement in classification performance and feature selection stability.http://www.aimspress.com/article/doi/10.3934/mbe.2021016?viewType=HTMLmutual informationfuzzy setsfuzzy mutual informationfeature selectionclassification systems |
| spellingShingle | Omar A. M. Salem Feng Liu Ahmed Sobhy Sherif Wen Zhang Xi Chen Feature selection based on fuzzy joint mutual information maximization Mathematical Biosciences and Engineering mutual information fuzzy sets fuzzy mutual information feature selection classification systems |
| title | Feature selection based on fuzzy joint mutual information maximization |
| title_full | Feature selection based on fuzzy joint mutual information maximization |
| title_fullStr | Feature selection based on fuzzy joint mutual information maximization |
| title_full_unstemmed | Feature selection based on fuzzy joint mutual information maximization |
| title_short | Feature selection based on fuzzy joint mutual information maximization |
| title_sort | feature selection based on fuzzy joint mutual information maximization |
| topic | mutual information fuzzy sets fuzzy mutual information feature selection classification systems |
| url | http://www.aimspress.com/article/doi/10.3934/mbe.2021016?viewType=HTML |
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