Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction
The data-driven management of real-life systems based on a trained model, which in turn is based on the data gathered from its daily usage, has attracted a lot of attention because it realizes scalable control for large-scale and complex systems. To obtain a model within an acceptable computational...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/6292143 |
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| _version_ | 1832554175078072320 |
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| author | Jaesung Lee Dae-Won Kim |
| author_facet | Jaesung Lee Dae-Won Kim |
| author_sort | Jaesung Lee |
| collection | DOAJ |
| description | The data-driven management of real-life systems based on a trained model, which in turn is based on the data gathered from its daily usage, has attracted a lot of attention because it realizes scalable control for large-scale and complex systems. To obtain a model within an acceptable computational cost that is restricted by practical constraints, the learning algorithm may need to identify essential data that carries important knowledge on the relation between the observed features representing the measurement value and labels encoding the multiple target concepts. This results in an increased computational burden owing to the concurrent learning of multiple labels. A straightforward approach to address this issue is feature selection; however, it may be insufficient to satisfy the practical constraints because the computational cost for feature selection can be impractical when the number of labels is large. In this study, we propose an efficient multilabel feature selection method to achieve scalable multilabel learning when the number of labels is large. The empirical experiments on several multilabel datasets show that the multilabel learning process can be boosted without deteriorating the discriminating power of the multilabel classifier. |
| format | Article |
| id | doaj-art-c0865af4b766421cbbd364de9c621878 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c0865af4b766421cbbd364de9c6218782025-02-03T05:52:16ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/62921436292143Scalable Multilabel Learning Based on Feature and Label Dimensionality ReductionJaesung Lee0Dae-Won Kim1Chung-Ang University, Seoul, Republic of KoreaThe School of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of KoreaThe data-driven management of real-life systems based on a trained model, which in turn is based on the data gathered from its daily usage, has attracted a lot of attention because it realizes scalable control for large-scale and complex systems. To obtain a model within an acceptable computational cost that is restricted by practical constraints, the learning algorithm may need to identify essential data that carries important knowledge on the relation between the observed features representing the measurement value and labels encoding the multiple target concepts. This results in an increased computational burden owing to the concurrent learning of multiple labels. A straightforward approach to address this issue is feature selection; however, it may be insufficient to satisfy the practical constraints because the computational cost for feature selection can be impractical when the number of labels is large. In this study, we propose an efficient multilabel feature selection method to achieve scalable multilabel learning when the number of labels is large. The empirical experiments on several multilabel datasets show that the multilabel learning process can be boosted without deteriorating the discriminating power of the multilabel classifier.http://dx.doi.org/10.1155/2018/6292143 |
| spellingShingle | Jaesung Lee Dae-Won Kim Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction Complexity |
| title | Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction |
| title_full | Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction |
| title_fullStr | Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction |
| title_full_unstemmed | Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction |
| title_short | Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction |
| title_sort | scalable multilabel learning based on feature and label dimensionality reduction |
| url | http://dx.doi.org/10.1155/2018/6292143 |
| work_keys_str_mv | AT jaesunglee scalablemultilabellearningbasedonfeatureandlabeldimensionalityreduction AT daewonkim scalablemultilabellearningbasedonfeatureandlabeldimensionalityreduction |