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Applications of Boolean Kernels in Rough Sets

  • Sinh Hoa Nguyen
  • Hung Son Nguyen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8537)

Abstract

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.

Keywords

Rough sets SVM Boolean Kernel Hybrid Systems 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sinh Hoa Nguyen
    • 1
    • 2
  • Hung Son Nguyen
    • 1
  1. 1.The University of WarsawWarsawPoland
  2. 2.Polish-Japanese Institute of Inf. TechnologyWarsawPoland

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