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| Titel: |
A parameter-free approach for one-class classification using binary decision diagrams |
| Auteur: |
Kutsuna, Takuro Yamamoto, Akihiro |
| Verschenen in: |
Intelligent data analysis |
| Paginering: | Jaargang 18 (2014) nr. 5 pagina's 889-910 |
| Jaar: |
2014-09-23 |
| Inhoud: |
Parameter tuning is essential in classification problems to achieve a high performance, but it is very hard when it comes to the one-class classification problem. In this paper, we propose a novel one-class classifier whose parameter can be tuned automatically. The proposed classifier can deal with non-linearly distributed data and is robust to noise in training data sets. Moreover, the proposed classifier can be learnt efficiently in the case that a training data set is large, because the computational complexity is approximately linear with respect to the number of training data. In the proposed method, the region of a training data set is expressed as a Boolean formula that is constructed by using a binary decision diagram. Then the region is efficiently over-approximated through the direct manipulation of the binary decision diagram. The parameter of the over-approximation can be tuned automatically based on the minimum description length principle. Experimental results show that the proposed method works very well with synthetic data and some realistic data. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
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