Earticle

초록

영어

This paper presents a new hybrid classifier that combines the k-Nearest Neighbor (k-NN) distance based algorithm with the classification tree paradigm based on the ID3 algorithm. The k-NN algorithm is used as a preprocessing algorithm in order to obtain a modified training database for the posterior learning of the classification tree structure. Then the incorrectly classified instances are duplicated with the previous data set and finally ID3 is applied to complete the classification procedure of biomedical data. In this approach a boosting technique is incorporated in such way that the incorrectly classified instances in the training set are identified using the k –NN algorithm. The performance of the proposed method is compared with the related algorithms. Experimental results show that the newly proposed approach performs better than the other existing techniques.

목차

Abstract
 1. Introduction
 2. Overview of the Work
 3. Decision Tree Algorithm
  3.1. Decision Tree Induction Using ID3 Algorithm
  3.2. Attribute Selection
  3.3. Tree Pruning
  3.4. Review Stage
  3.5. Boosting Using k-NN Algorithm
 4. Features of k-NN
  4.1. Decision Rule and Confusion Matrix for Classification
  4.2. Performance Assessment with Cross-Validation
 5. Result and Discussion
 6. Conclusions
 References

키워드

저자

• Shovon K. Pramanik [ Dept. of Computer Science and Engineering, University of Rajshahi, Bangladeh ]
• Subrata Pramanik [ Dept. of Computer Science and Engineering, University of Rajshahi, Bangladeh ]
• Bimal K. Pramanik [ Dept. of Computer Science and Engineering, University of Rajshahi, Bangladeh ]
• M. K. Islam Molla [ Dept. of Computer Science and Engineering, University of Rajshahi, Bangladeh ]
• Md. Ekramul Hamid [ Dept. of Computer Science and Engineering, University of Rajshahi, Bangladeh ]

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