A Discretization Algorithm Based on Forest Optimization Network and Variable Precision Rough Set
Discretization of multidimensional attributes can improve the training speed and accuracy of machine learning algorithm. At present, the discretization algorithms perform at a lower level, and most of them are single attribute discretization algorithm, ignoring the potential association between attr...
| Format: | Article |
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| Language: | zho |
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EDP Sciences
2020-04-01
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| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2020/02/jnwpu2020382p434/jnwpu2020382p434.html |
| _version_ | 1797421127721549824 |
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| collection | DOAJ |
| description | Discretization of multidimensional attributes can improve the training speed and accuracy of machine learning algorithm. At present, the discretization algorithms perform at a lower level, and most of them are single attribute discretization algorithm, ignoring the potential association between attributes. Based on this, we proposed a discretization algorithm based on forest optimization and rough set (FORDA) in this paper. To solve the problem of discretization of multi-dimensional attributes, the algorithm designs the appropriate value function according to the variable precision rough set theory, and then constructs the forest optimization network and iteratively searches for the optimal subset of breakpoints. The experimental results on the UCI datasets show that:compared with the current mainstream discretization algorithms, the algorithm can avoid local optimization, significantly improve the classification accuracy of the SVM classifier, and its discretization performance is better, which verifies the effectiveness of the algorithm. |
| first_indexed | 2024-03-09T07:11:28Z |
| format | Article |
| id | doaj.art-82e7375341ad4adbbb8551dbffdc8317 |
| institution | Directory Open Access Journal |
| issn | 1000-2758 2609-7125 |
| language | zho |
| last_indexed | 2024-03-09T07:11:28Z |
| publishDate | 2020-04-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj.art-82e7375341ad4adbbb8551dbffdc83172023-12-03T09:03:53ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252020-04-0138243444110.1051/jnwpu/20203820434jnwpu2020382p434A Discretization Algorithm Based on Forest Optimization Network and Variable Precision Rough Set0123School Computer Science and Technology, Harbin Engineering UniversitySchool Computer Science and Technology, Harbin Engineering UniversitySchool Computer Science and Technology, Harbin Engineering UniversitySchool Computer Science and Technology, Harbin Engineering UniversityDiscretization of multidimensional attributes can improve the training speed and accuracy of machine learning algorithm. At present, the discretization algorithms perform at a lower level, and most of them are single attribute discretization algorithm, ignoring the potential association between attributes. Based on this, we proposed a discretization algorithm based on forest optimization and rough set (FORDA) in this paper. To solve the problem of discretization of multi-dimensional attributes, the algorithm designs the appropriate value function according to the variable precision rough set theory, and then constructs the forest optimization network and iteratively searches for the optimal subset of breakpoints. The experimental results on the UCI datasets show that:compared with the current mainstream discretization algorithms, the algorithm can avoid local optimization, significantly improve the classification accuracy of the SVM classifier, and its discretization performance is better, which verifies the effectiveness of the algorithm.https://www.jnwpu.org/articles/jnwpu/full_html/2020/02/jnwpu2020382p434/jnwpu2020382p434.htmldiscretizationforest optimization networkmultiple dimensionsvariable precision rough setbreakpoint subsetnonlinear systemssvmalgorithms |
| spellingShingle | A Discretization Algorithm Based on Forest Optimization Network and Variable Precision Rough Set Xibei Gongye Daxue Xuebao discretization forest optimization network multiple dimensions variable precision rough set breakpoint subset nonlinear systems svm algorithms |
| title | A Discretization Algorithm Based on Forest Optimization Network and Variable Precision Rough Set |
| title_full | A Discretization Algorithm Based on Forest Optimization Network and Variable Precision Rough Set |
| title_fullStr | A Discretization Algorithm Based on Forest Optimization Network and Variable Precision Rough Set |
| title_full_unstemmed | A Discretization Algorithm Based on Forest Optimization Network and Variable Precision Rough Set |
| title_short | A Discretization Algorithm Based on Forest Optimization Network and Variable Precision Rough Set |
| title_sort | discretization algorithm based on forest optimization network and variable precision rough set |
| topic | discretization forest optimization network multiple dimensions variable precision rough set breakpoint subset nonlinear systems svm algorithms |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2020/02/jnwpu2020382p434/jnwpu2020382p434.html |