상세정보
학술대회
Pruning with Majority and Minority Properties
- 저자
- 전해숙, 이원돈
- 발행일
- 201405
- 출처
- International Conference on Information Science and Applications (ICISA) 2014, pp.1-4
- 협약과제
- 14ZI1200, 고품격 미래인터넷을위한 식별자기반 네트워킹기술연구, 정희영
- 초록
- Classification is very imprtant research in knowledge discovery and machine learning. The decision tree is one of the well-known data mining methods. In general, a decision tree can be grown so as to have zero eeor on the training data set. If there is any noise in the data set or it does not completely cover the decision space, then over-fitting occurs and the tree needs to be pruned in order to accurately generalize the test data set. In this paper, we propose a pre-pruning method with majority and minority properties for the decision tree. It uses two kinds of qualifying criteria to consider whether the ration of the highest class of a subtree is the majority of the subtree or a minority of the overall tree. New measures for these are added to the classifier with the extended data expression. Experiments show that a clasifier using this pruning method can improve classification accuracy as well as reduce the size of the tree. © 2014 IEEE.
- KSP 제안 키워드
- Data mining(DM), Data mining methods, Data sets, Decision Tree(DT), Knowledge discovery, Pre-pruning, Pruning method, Test data, classification accuracy, known data, machine Learning