Earticle

초록

영어

The reduced support vector machine (RSVM) is extension method of smooth support vector machine (SSVM) for handling computational difficulties as well as reduces the model complexity by generating a nonlinear separating surface for a large dataset. To generate representative reduce set for RSVM, clustering reduced support vector machine (CRSVM) was proposed. However, CRSVM is restricted to solve classification problems for large dataset with numeric attributes. In this paper, we propose an alternative algorithm, k-mode RSVM (KMO-RSVM) that combines RSVM and k-mode clustering technique to handle classification problems on categorical large dataset. Applying k-mode clustering algorithm to each class, we can generate cluster centroids of each class and use them to form the reduced set which is used in RSVM. In our experiments, we tested the effectiveness of KMO-RSVM on four public available dataset. It turns out that KMO-RSVM can improve speed of running time significantly than SSVM and still obtained a high accuracy. Comparison with RSVM indicates that KMO-RSVM is faster, gets smaller reduced set and comparable testing accuracy than RSVM.

목차

Abstract
 1. Introduction
 2. Reduced support vector machine (RSVM)
  2.1. Fundamental concept
  2.2. Review of RSVM
 3. RSVM based on k-mode clustering
  3.1. K-mode Clustering
  3.2. KMO-RSVM
 4. Experiments and results
 5. Conclusions and discussion
 References

키워드

저자

• Santi Wulan Purnami [ Department of Statistics, Institut Teknologi Sepuluh Nopember (ITS) Surabaya ]
• Jasni Mohamad Zain [ Faculty of Computer System and Software Engineering, University Malaysia Pahang ]
• Tutut Heriawan [ Faculty of Computer System and Software Engineering, University Malaysia Pahang ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605