Hierarchically penalized support vector machine with grouped variables
| Autor: | Myoungshic Jhun, Jongkyeong Kang, Eunkyung Kim, Sungwan Bang |
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| Rok vydání: | 2016 |
| Předmět: |
Computer Science::Machine Learning
Computational intelligence Feature selection Linear classifier 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Statistics::Machine Learning 010104 statistics & probability Lasso (statistics) Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 0101 mathematics Mathematics business.industry Model selection Pattern recognition Support vector machine ComputingMethodologies_PATTERNRECOGNITION Group selection Pattern recognition (psychology) 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business computer Software |
| Zdroj: | International Journal of Machine Learning and Cybernetics. 8:1211-1221 |
| ISSN: | 1868-808X 1868-8071 |
| DOI: | 10.1007/s13042-016-0494-2 |
| Popis: | When input features are naturally grouped or generated by factors in a linear classification problem, it is more meaningful to identify important groups or factors rather than individual features. The F ∞-norm support vector machine (SVM) and the group lasso penalized SVM have been developed to perform simultaneous classification and factor selection. However, these group-wise penalized SVM methods may suffer from estimation inefficiency and model selection inconsistency because they cannot perform feature selection within an identified group. To overcome this limitation, we propose the hierarchically penalized SVM (H-SVM) that not only effectively identifies important groups but also removes irrelevant features within an identified group. Numerical results are presented to demonstrate the competitive performance of the proposed H-SVM over existing SVM methods. |
| Databáze: | OpenAIRE |
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