Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning
This paper presents a new hybrid discriminant analysis method, and this method combines the ideas of linearity and nonlinearity to establish a two-layer discriminant model. The first layer is a linear discriminant model, which is mainly used to determine the distinguishable samples and subsample; th...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/1512391 |
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| _version_ | 1832553938972311552 |
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| author | Liwen Huang |
| author_facet | Liwen Huang |
| author_sort | Liwen Huang |
| collection | DOAJ |
| description | This paper presents a new hybrid discriminant analysis method, and this method combines the ideas of linearity and nonlinearity to establish a two-layer discriminant model. The first layer is a linear discriminant model, which is mainly used to determine the distinguishable samples and subsample; the second layer is a nonlinear discriminant model, which is used to determine the subsample type. Numerical experiments on real data sets show that this method performs well compared to other classification algorithms, and its stability is better than the common discriminant models. |
| format | Article |
| id | doaj-art-450f129439fa46f2a0a301bffa9c9aea |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-450f129439fa46f2a0a301bffa9c9aea2025-02-03T05:52:44ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/15123911512391Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine LearningLiwen Huang0College of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, Fujian, ChinaThis paper presents a new hybrid discriminant analysis method, and this method combines the ideas of linearity and nonlinearity to establish a two-layer discriminant model. The first layer is a linear discriminant model, which is mainly used to determine the distinguishable samples and subsample; the second layer is a nonlinear discriminant model, which is used to determine the subsample type. Numerical experiments on real data sets show that this method performs well compared to other classification algorithms, and its stability is better than the common discriminant models.http://dx.doi.org/10.1155/2020/1512391 |
| spellingShingle | Liwen Huang Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning Discrete Dynamics in Nature and Society |
| title | Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning |
| title_full | Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning |
| title_fullStr | Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning |
| title_full_unstemmed | Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning |
| title_short | Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning |
| title_sort | modified hybrid discriminant analysis methods and their applications in machine learning |
| url | http://dx.doi.org/10.1155/2020/1512391 |
| work_keys_str_mv | AT liwenhuang modifiedhybriddiscriminantanalysismethodsandtheirapplicationsinmachinelearning |