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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Applications of Boolean Kernels in Rough Sets
verfasst von : Sinh Hoa Nguyen, Hung Son Nguyen
Erschienen in: Rough Sets and Intelligent Systems Paradigms
Verlag: Springer International Publishing
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Rough Sets (RS) and Support Vector Machine (SVM) are the two big and independent research areas in AI. Originally, rough set theory is dealing with the concept approximation problem under uncertainty. The basic idea of RS is related to lower and upper approximations, and it can be applied in classification problem. At the first sight RS and SVM offer different approaches to classification problem. Most RS methods are based on minimal decision rules, while SVM converts the linear classifiers into instance based classifiers. This paper presents a comparison analysis between these areas and shows that, despite differences, there are quite many analogies in the two approaches. We will show that some rough set classifiers are in fact the SVM with Boolean kernel and propose some hybrid methods that combine the advantages of those two great machine learning approaches.