Applications of Boolean Kernels in Rough Sets
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.
KeywordsRough sets SVM Boolean Kernel Hybrid Systems
Unable to display preview. Download preview PDF.
- 2.Bazan, J.: A Comparison of Dynamic and non-Dynamic Rough Set Methods for Extracting Laws from Decision Tables. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications. STUDFUZZ, vol. 18, pp. 321–365. Springer, Heidelberg (1998)Google Scholar
- 7.Li, Y., Cai, Y., Li, Y., Xu, X.: Rough sets method for SVM data preprocessing. In: Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, pp. 1039–1042 (2004)Google Scholar
- 8.Lingras, P., Butz, C.J.: Interval set classifiers using support vector machines. In: Proc. the North American Fuzzy Inform. Processing Society Conference, pp. 707–710 (2004)Google Scholar