Feature selection in machine learning: A new perspective
High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, an...
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| Published in: | Neurocomputing (Amsterdam) Vol. 300; pp. 70 - 79 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
26.07.2018
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| Subjects: | |
| ISSN: | 0925-2312, 1872-8286 |
| Online Access: | Get full text |
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| Abstract | High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, and facilitate a better understanding for the learning model or data. In this study, we discuss several frequently-used evaluation measures for feature selection, and then survey supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering. Lastly, future challenges about feature selection are discussed. |
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| AbstractList | High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, and facilitate a better understanding for the learning model or data. In this study, we discuss several frequently-used evaluation measures for feature selection, and then survey supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering. Lastly, future challenges about feature selection are discussed. |
| Author | Yang, Sheng Luo, Jiawei Wang, Shulin Cai, Jie |
| Author_xml | – sequence: 1 givenname: Jie surname: Cai fullname: Cai, Jie – sequence: 2 givenname: Jiawei surname: Luo fullname: Luo, Jiawei – sequence: 3 givenname: Shulin surname: Wang fullname: Wang, Shulin – sequence: 4 givenname: Sheng surname: Yang fullname: Yang, Sheng email: yangsh0506@sina.com |
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