A novel selective naïve Bayes algorithm
Naïve Bayes is one of the most popular data mining algorithms. Its efficiency comes from the assumption of attribute independence, although this might be violated in many real-world data sets. Many efforts have been done to mitigate the assumption, among which attribute selection is an important app...
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| Published in | Knowledge-based systems Vol. 192; p. 105361 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
15.03.2020
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0950-7051 1872-7409 |
| DOI | 10.1016/j.knosys.2019.105361 |
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| Abstract | Naïve Bayes is one of the most popular data mining algorithms. Its efficiency comes from the assumption of attribute independence, although this might be violated in many real-world data sets. Many efforts have been done to mitigate the assumption, among which attribute selection is an important approach. However, conventional efforts to perform attribute selection in naïve Bayes suffer from heavy computational overhead. This paper proposes an efficient selective naïve Bayes algorithm, which adopts only some of the attributes to construct selective naïve Bayes models. These models are built in such a way that each one is a trivial extension of another. The most predictive selective naïve Bayes model can be selected by the measures of incremental leave-one-out cross validation. As a result, attributes can be selected by efficient model selection. Empirical results demonstrate that the selective naïve Bayes shows superior classification accuracy, yet at the same time maintains the simplicity and efficiency. |
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| AbstractList | Naïve Bayes is one of the most popular data mining algorithms. Its efficiency comes from the assumption of attribute independence, although this might be violated in many real-world data sets. Many efforts have been done to mitigate the assumption, among which attribute selection is an important approach. However, conventional efforts to perform attribute selection in naïve Bayes suffer from heavy computational overhead. This paper proposes an efficient selective naïve Bayes algorithm, which adopts only some of the attributes to construct selective naïve Bayes models. These models are built in such a way that each one is a trivial extension of another. The most predictive selective naïve Bayes model can be selected by the measures of incremental leave-one-out cross validation. As a result, attributes can be selected by efficient model selection. Empirical results demonstrate that the selective naïve Bayes shows superior classification accuracy, yet at the same time maintains the simplicity and efficiency. |
| ArticleNumber | 105361 |
| Author | Chen, Shenglei Webb, Geoffrey I. Ma, Xin Liu, Linyuan |
| Author_xml | – sequence: 1 givenname: Shenglei surname: Chen fullname: Chen, Shenglei email: shenglei.chen@nau.edu.cn organization: Department of E-Commerce, Nanjing Audit University, Nanjing, China – sequence: 2 givenname: Geoffrey I. surname: Webb fullname: Webb, Geoffrey I. organization: Faculty of Information Technology, Monash University, VIC 3800, Australia – sequence: 3 givenname: Linyuan surname: Liu fullname: Liu, Linyuan organization: Department of E-Commerce, Nanjing Audit University, Nanjing, China – sequence: 4 givenname: Xin surname: Ma fullname: Ma, Xin organization: School of Statistics and Mathematics, Nanjing Audit University, Nanjing, China |
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