Earticle

초록

영어

Associative classification is a supervised classification approach, integrating association mining and classification. Several studies in data mining have shown that associative classification achieves higher classification accuracy than do traditional classification techniques. However, the associative classification suffers from a major drawback: The huge number of the generated classification rules which takes efforts to select the best ones in order to construct the classifier. To overcome such drawback, we have proposed an associative classification method that reduces associative classification rules without jeopardizing the classification accuracy. Moreover, we will introduce in this paper two different strategies to classify new instances based on some interestingness measures that arise from data mining literature in order to select the best rules during classification. A detailed description of this method is presented in this paper, as well as the experimentation study on 12 benchmark data sets proving that our approach is highly competitive in terms of accuracy in comparison with popular classification approaches.

목차

Abstract
 1. Introduction
 2. Basic Notions and Related Work
  2.1 Basic Notions
  2.2 Related Work
 3. Our Proposed Approach Based on Generic Associative Classification Rules
  3.1 Basic Definitions
  3.2 Learning Stage
  3.3 Classifying Stage
 4. Experimental Study
  4.1 Variation of Interestingness Measures Thresholds
  4.2 Generic Classification Rules Impact
  4.3 Robustness of Our Proposed Approach
 5. Conclusion
 References

키워드

저자

• I. Bouzouita [ Computer Science Department, 1060 Tunis, Tunisia. ]
• S. Elloumi [ Computer Science Department, 1060 Tunis, Tunisia. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605