Counter-Example Generation-Based One-Class Classification
| Autor: | András Kocsor, Róbert Busa-Fekete, András Bánhalmi |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
business.industry
Pattern recognition Mixture model Machine learning computer.software_genre Support vector machine Set (abstract data type) ComputingMethodologies_PATTERNRECOGNITION Binary classification Feature (machine learning) Decision boundary One-class classification Artificial intelligence business computer Mathematics Counterexample |
| Zdroj: | Machine Learning: ECML 2007 ISBN: 9783540749578 ECML |
| DOI: | 10.1007/978-3-540-74958-5_51 |
| Popis: | For One-Class Classification problems several methods have been proposed in the literature. These methods all have the common feature that the decision boundary is learnt by just using a set of the positive examples. Here we propose a method that extends the training set with a counter-example set, which is generated directly using the set of positive examples. Using the extended training set, a binary classifier (here i¾?-SVM) is applied to separate the positive and the negative points. The results of this novel technique are compared with those of One-Class SVM and the Gaussian Mixture Model on several One-Class Classification tasks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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