Multiobjective feature selection for microarray data via distributed parallel algorithms
Many real-world problems are large in scale and hence difficult to address. Due to the large number of features in microarray datasets, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some...
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| Vydané v: | Future generation computer systems Ročník 100; s. 952 - 981 |
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| Jazyk: | English |
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Elsevier B.V
01.11.2019
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| ISSN: | 0167-739X, 1872-7115 |
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| Abstract | Many real-world problems are large in scale and hence difficult to address. Due to the large number of features in microarray datasets, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Through feature selection, a feature subset that contains only a small quantity of essential features can be generated to increase the classification accuracy and significantly reduce the time consumption. In this paper, we construct a multiobjective feature selection model that simultaneously considers the classification error, the feature number and the feature redundancy. For this model, we propose several distributed parallel algorithms based on different encodings and an adaptive strategy. Additionally, to reduce the time consumption, various tactics are employed, including a feature number constraint, distributed parallelism and sample-wise parallelism. For a batch of microarray datasets, the proposed algorithms are superior to several state-of-the-art multiobjective evolutionary algorithms in terms of both effectiveness and efficiency.
•A multi-objective feature selection model is presented and tackled.•Algorithms with two encoding methodologies are proposed.•Adaptive technique is explored.•Explicit feature number threshold and distributed parallelism are employed for efficiency. |
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| AbstractList | Many real-world problems are large in scale and hence difficult to address. Due to the large number of features in microarray datasets, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Through feature selection, a feature subset that contains only a small quantity of essential features can be generated to increase the classification accuracy and significantly reduce the time consumption. In this paper, we construct a multiobjective feature selection model that simultaneously considers the classification error, the feature number and the feature redundancy. For this model, we propose several distributed parallel algorithms based on different encodings and an adaptive strategy. Additionally, to reduce the time consumption, various tactics are employed, including a feature number constraint, distributed parallelism and sample-wise parallelism. For a batch of microarray datasets, the proposed algorithms are superior to several state-of-the-art multiobjective evolutionary algorithms in terms of both effectiveness and efficiency.
•A multi-objective feature selection model is presented and tackled.•Algorithms with two encoding methodologies are proposed.•Adaptive technique is explored.•Explicit feature number threshold and distributed parallelism are employed for efficiency. |
| Author | Cao, Bin Yang, Peng Elhoseny, Mohamed Qi, Jun Simpson, Andrew Zhao, Jianwei Liu, Xin Mehmood, Irfan Muhammad, Khan Yang, Po |
| Author_xml | – sequence: 1 givenname: Bin surname: Cao fullname: Cao, Bin organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, China – sequence: 2 givenname: Jianwei surname: Zhao fullname: Zhao, Jianwei email: 201422102003@stu.hebut.edu.cn organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, China – sequence: 3 givenname: Po surname: Yang fullname: Yang, Po email: poyangcn@gmail.com organization: Department of Computer Science, Liverpool John Moores University, UK – sequence: 4 givenname: Peng surname: Yang fullname: Yang, Peng organization: School of Artificial Intelligence, Hebei University of Technology, China – sequence: 5 givenname: Xin surname: Liu fullname: Liu, Xin organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, China – sequence: 6 givenname: Jun surname: Qi fullname: Qi, Jun organization: Department of Computer Science, Liverpool John Moores University, UK – sequence: 7 givenname: Andrew surname: Simpson fullname: Simpson, Andrew organization: Department of Computer Science, Liverpool John Moores University, UK – sequence: 8 givenname: Mohamed surname: Elhoseny fullname: Elhoseny, Mohamed organization: Faculty of Computers and Information, Mansoura University, Egypt – sequence: 9 givenname: Irfan surname: Mehmood fullname: Mehmood, Irfan organization: Department of Media Design and Technology, Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, United Kingdom – sequence: 10 givenname: Khan surname: Muhammad fullname: Muhammad, Khan organization: Department of Software, Sejong University, Seoul 143-747, Republic of Korea |
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| Keywords | Microarray dataset High dimension Multiobjective feature selection Distributed parallelism Feature redundancy |
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| SubjectTerms | Distributed parallelism Feature redundancy High dimension Microarray dataset Multiobjective feature selection |
| Title | Multiobjective feature selection for microarray data via distributed parallel algorithms |
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