Name
Title | ||
|---|---|---|
A parameter-free approach for one-class classification using binary decision diagrams |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3233/IDA-140674 | Intell. Data Anal. |
Keywords | Field | DocType |
binary decision diagram,minimum description length principle,one-class classification,one class classification | Training set,One-class classification,Pattern recognition,Computer science,Minimum description length,Binary decision diagram,Synthetic data,Artificial intelligence,True quantified Boolean formula,Classifier (linguistics),Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
18 | 5 | 1088-467X |
Citations | PageRank | References |
2 | 0.39 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takuro Kutsuna | 1 | 11 | 5.00 |
| Akihiro Yamamoto | 2 | 135 | 26.84 |