One-Class Support Vector Machines For Protein-Protein Interactions Prediction

It has been previously shown that the matching performance of a multimodal biometric system can be improved by using user-specific fusion. The objective of this approach is to address the fact that some users are difficult to recognize using some biometric traits, while these traits are highly discriminant for others. Conventional two-class classification methods, when used to design user-specific fusion, often suffer from the problem of limited availability of training data, especially, those of genuine users. In this paper, we propose a user-specific fusion approach, making use of one-class classifiers, known as boundary methods, to avoid the aforementioned problem of the two-class classification approach. We also show that such an approach outperforms others, including the Sum of Scores, the standard SVM, and the one-class SVM[10], in experiments carried out on the BioSecure DS2 database.

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Designing a compact Genetic fuzzy rule-based system for one-class classification

Villar

Krawczyk

Sánchez

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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This paper proposes a method for designing Fuzzy Rule-Based Classification Systems to deal with One-Class Classification, where during the training phase we have access only to objects originating from a single class. However, the trained model must be prepared to deal with new, unseen adversarial objects, known as outliers. We use a Genetic Algorithm for learning the granularity, domains and fuzzy partitions of the model and we propose an ad-hoc rule generation method specific for One-Class Classification. Several datasets from UCI repository, previously transformed to one-class problems, are used in the experiments and we compare with two of the classical methods used in the One-Class community, one-class Support Vector Machines and Support Vector Data Description. Our proposal of fuzzy model obtains similar results than the other methods but presents a high interpretability due its reduced number of rules.

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One-Class Support Vector Machines For Protein-Protein Interactions Prediction

Cited by 6 publications

References 27 publications

Vision-based obstacle detection using a support vector machine

Vision-based obstacle detection using a support vector machine

User-Specific Fusion Using One-Class Classification for Multimodal Biometric Systems: Boundary Methods

Designing a compact Genetic fuzzy rule-based system for one-class classification

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