Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors
Feature selection is an essential process in data mining applications since it reduces a model’s complexity. However, feature selection with various types of costs is still a new research topic. In this paper, we study the cost-sensitive feature selection problem of numeric data with measurement err...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/754698 |
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| _version_ | 1832556103314964480 |
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| author | Hong Zhao Fan Min William Zhu |
| author_facet | Hong Zhao Fan Min William Zhu |
| author_sort | Hong Zhao |
| collection | DOAJ |
| description | Feature selection is an essential process in data mining applications since it reduces a model’s complexity. However, feature selection with various types of costs is still a new research topic. In this paper, we study the cost-sensitive feature selection problem of numeric data with measurement errors. The major contributions of this paper are fourfold. First, a new data model is built to address test costs and misclassification costs as well as error boundaries. It is distinguished from the existing models mainly on the error boundaries. Second, a covering-based rough set model with normal distribution measurement errors is constructed. With this model, coverings are constructed from data rather than assigned by users. Third, a new cost-sensitive feature selection problem is defined on this model. It is more realistic than the existing feature selection problems. Fourth, both backtracking and heuristic algorithms are proposed to deal with the new problem. Experimental results show the efficiency of the pruning techniques for the backtracking algorithm and the effectiveness of the heuristic algorithm. This study is a step toward realistic applications of the cost-sensitive learning. |
| format | Article |
| id | doaj-art-06b71b6144df4274a6b9b31d896f4ae4 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-06b71b6144df4274a6b9b31d896f4ae42025-02-03T05:46:21ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/754698754698Cost-Sensitive Feature Selection of Numeric Data with Measurement ErrorsHong Zhao0Fan Min1William Zhu2Laboratory of Granular Computing, Zhangzhou Normal University, Zhangzhou 363000, ChinaLaboratory of Granular Computing, Zhangzhou Normal University, Zhangzhou 363000, ChinaLaboratory of Granular Computing, Zhangzhou Normal University, Zhangzhou 363000, ChinaFeature selection is an essential process in data mining applications since it reduces a model’s complexity. However, feature selection with various types of costs is still a new research topic. In this paper, we study the cost-sensitive feature selection problem of numeric data with measurement errors. The major contributions of this paper are fourfold. First, a new data model is built to address test costs and misclassification costs as well as error boundaries. It is distinguished from the existing models mainly on the error boundaries. Second, a covering-based rough set model with normal distribution measurement errors is constructed. With this model, coverings are constructed from data rather than assigned by users. Third, a new cost-sensitive feature selection problem is defined on this model. It is more realistic than the existing feature selection problems. Fourth, both backtracking and heuristic algorithms are proposed to deal with the new problem. Experimental results show the efficiency of the pruning techniques for the backtracking algorithm and the effectiveness of the heuristic algorithm. This study is a step toward realistic applications of the cost-sensitive learning.http://dx.doi.org/10.1155/2013/754698 |
| spellingShingle | Hong Zhao Fan Min William Zhu Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors Journal of Applied Mathematics |
| title | Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors |
| title_full | Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors |
| title_fullStr | Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors |
| title_full_unstemmed | Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors |
| title_short | Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors |
| title_sort | cost sensitive feature selection of numeric data with measurement errors |
| url | http://dx.doi.org/10.1155/2013/754698 |
| work_keys_str_mv | AT hongzhao costsensitivefeatureselectionofnumericdatawithmeasurementerrors AT fanmin costsensitivefeatureselectionofnumericdatawithmeasurementerrors AT williamzhu costsensitivefeatureselectionofnumericdatawithmeasurementerrors |