TS-WRSVM: twin structural weighted relaxed support vector machine
Classification of data with imbalanced class distributions is a major problem in the data mining community. Imbalanced classification is a challenging task in the presence of outliers. In this paper, we propose a new cost-sensitive learning method with regard to the structure of data distribution fo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2019-07-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2019.1573418 |
| _version_ | 1797684105788260352 |
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| author | Fatemeh Sheykh Mohammadi Ali Amiri |
| author_facet | Fatemeh Sheykh Mohammadi Ali Amiri |
| author_sort | Fatemeh Sheykh Mohammadi |
| collection | DOAJ |
| description | Classification of data with imbalanced class distributions is a major problem in the data mining community. Imbalanced classification is a challenging task in the presence of outliers. In this paper, we propose a new cost-sensitive learning method with regard to the structure of data distribution for classifying imbalanced data and diminishing the effect of outliers. The proposed method combines the benefits of “structured” learning models (such as structural support vector machine) with the advantages of “cost-sensitive” learning models (such as weighted relaxed support vector machine). We call our method twin structural weighted relaxed support vector machine (TS-WRSVM). A TS-WRSVM uses two nonparallel hyperplanes to determine the class label of new data so that each model only considers the structural information of one class. We allocate a weight and a limited amount of penalty-free slack to each model by considering the size of each class. Results of experiments indicate that a TS-WRSVM is superior to other current algorithms based on cost-sensitive learning in the areas of classification accuracy and computational time. |
| first_indexed | 2024-03-12T00:24:46Z |
| format | Article |
| id | doaj.art-f035ef72bede4a099bf153a4d5e32499 |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:24:46Z |
| publishDate | 2019-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-f035ef72bede4a099bf153a4d5e324992023-09-15T10:47:58ZengTaylor & Francis GroupConnection Science0954-00911360-04942019-07-0131321524310.1080/09540091.2019.15734181573418TS-WRSVM: twin structural weighted relaxed support vector machineFatemeh Sheykh Mohammadi0Ali Amiri1University of ZanjanUniversity of ZanjanClassification of data with imbalanced class distributions is a major problem in the data mining community. Imbalanced classification is a challenging task in the presence of outliers. In this paper, we propose a new cost-sensitive learning method with regard to the structure of data distribution for classifying imbalanced data and diminishing the effect of outliers. The proposed method combines the benefits of “structured” learning models (such as structural support vector machine) with the advantages of “cost-sensitive” learning models (such as weighted relaxed support vector machine). We call our method twin structural weighted relaxed support vector machine (TS-WRSVM). A TS-WRSVM uses two nonparallel hyperplanes to determine the class label of new data so that each model only considers the structural information of one class. We allocate a weight and a limited amount of penalty-free slack to each model by considering the size of each class. Results of experiments indicate that a TS-WRSVM is superior to other current algorithms based on cost-sensitive learning in the areas of classification accuracy and computational time.http://dx.doi.org/10.1080/09540091.2019.1573418imbalanced data classificationcost-sensitive learningstructured learningts-wrsvmoutliers |
| spellingShingle | Fatemeh Sheykh Mohammadi Ali Amiri TS-WRSVM: twin structural weighted relaxed support vector machine Connection Science imbalanced data classification cost-sensitive learning structured learning ts-wrsvm outliers |
| title | TS-WRSVM: twin structural weighted relaxed support vector machine |
| title_full | TS-WRSVM: twin structural weighted relaxed support vector machine |
| title_fullStr | TS-WRSVM: twin structural weighted relaxed support vector machine |
| title_full_unstemmed | TS-WRSVM: twin structural weighted relaxed support vector machine |
| title_short | TS-WRSVM: twin structural weighted relaxed support vector machine |
| title_sort | ts wrsvm twin structural weighted relaxed support vector machine |
| topic | imbalanced data classification cost-sensitive learning structured learning ts-wrsvm outliers |
| url | http://dx.doi.org/10.1080/09540091.2019.1573418 |
| work_keys_str_mv | AT fatemehsheykhmohammadi tswrsvmtwinstructuralweightedrelaxedsupportvectormachine AT aliamiri tswrsvmtwinstructuralweightedrelaxedsupportvectormachine |