Earticle

초록

영어

Decision tree induction has gained its popularity as an effective automated method for data classification mainly because of its simple, easy-to-understand, and noise-tolerant characteristics. The induced tree reveals the most informative attributes that can best characterize training data and accurately predict classes of unseen data. Despite its predictive power, the tree structure can be overly expanded or deeply grown when the training data do not show explicit patterns. Such bushy and deep trees are difficult to comprehend and interpret by humans. We thus propose a logic-based method to query over a complicate tree structure to extract only parts of the tree model that are really relevant to users’ interest. The implementation using ECLiPSe constraint language to perform constrained search over a decision tree model is given in this paper. The illustrative examples on medical domains support our hypothesis regarding simplicity of constrained tree-based patterns.

목차

Abstract
 1. Introduction
 2. Building a Decision Tree Model with Logic Programming
 3. Querying a Tree Model
 4. Experimentation and Results
 5. Conclusion
 Acknowledgements
 Appendix
 References

키워드

저자

• Nittaya Kerdprasop [ Data Engineering Research Unit, School of Computer Engineering, Suranaree University of Technology ]
• Fonthip Koongaew [ Data Engineering Research Unit, School of Computer Engineering, Suranaree University of Technology ]
• Kittisak Kerdprasop [ Data Engineering Research Unit, School of Computer Engineering, Suranaree University of Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605