Earticle

초록

영어

In the paper, with the introduction of data mining algorithm of the classification in detail, and then combining the classification algorithm and incremental learning technology, an incremental decision tree algorithm is proposed to solve the problem of incremental learning and analysis the experimental data for this algorithm. The paper used ID3 and C4.5 algorithm for detailed research. According to two algorithms, combining Bayesian classification algorithm’s incremental learning characteristic, the paper proposed an incremental decision tree algorithm , and by the analysis of experimental data. This algorithm can solve the incremental learning problem of the decision tree algorithm very well.

목차

Abstract
 1. Introduction
 2. Overview of Decision Tree Classification Algorithms
  2.1 Decision Tree Construction Algorithm
  2.2. Decision Tree Pruning Algorithm
  2.3 ID3 Algorithm
  2.4 Analysis of the Advantages and Disadvantages of ID3 Algorithm
 3. Theoretical Foundation of Naive Bayesian Method
 4. Naive Bias Classification Theory
 5. Example of Application of Bias Theory
 6. Incremental Learning
 7. Implementation of Incremental Decision Tree Algorithm (HID)
  7.1. Interface of Bayesian Classifier
  7.2. Incremental Decision Tree Algorithm
 8. Experimental Analysis and Results
 9. Conclusion
 References

키워드

저자

• Wenyi Xu [ Heyuan Polytechnic, Heyuan, china ]

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