Interval SVM-Based Classification Algorithm Using the Uncertainty Trick
| Autor: | Yulia A. Zhuk, Lev V. Utkin |
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| Rok vydání: | 2017 |
| Předmět: |
Training set
business.industry Computer science Pattern recognition 02 engineering and technology Interval (mathematics) Machine learning computer.software_genre 01 natural sciences Support vector machine 010104 statistics & probability Binary classification Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence 0101 mathematics business computer Algorithm |
| Zdroj: | International Journal on Artificial Intelligence Tools. 26:1750014 |
| ISSN: | 1793-6349 0218-2130 |
| DOI: | 10.1142/s0218213017500142 |
| Popis: | A new robust SVM-based algorithm of the binary classification is proposed. It is based on the so-called uncertainty trick when training data with the interval uncertainty are transformed to training data with the weight or probabilistic uncertainty. Every interval is replaced by a set of training points with the same class label such that every point inside the interval has an unknown weight from a predefined set of weights. The robust strategy dealing with the upper bound of the interval-valued expected risk produced by a set of weights is used in the SVM. An extension of the algorithm based on using the imprecise Dirichlet model is proposed for its additional robustification. Numerical examples with synthetic and real interval-valued training data illustrate the proposed algorithm and its extension. |
| Databáze: | OpenAIRE |
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