상세정보
학술대회
Performance Measurement of Decision Tree Excluding Insignificant Leaf Nodes
- 저자
- 전해숙, 이원돈
- 발행일
- 201410
- 출처
- International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) 2014, pp.122-127
- 협약과제
- 14ZI1200, 고품격 미래인터넷을위한 식별자기반 네트워킹기술연구, 정희영
- 초록
- Too much information exist in ubiquitous environment, and therefore it is not easy to obtain the appropriately classified information from the available data set. Decision tree algorithm is useful in the field of data mining or machine learning system, as it is fast and deduces good result on the problem of classification. Sometimes, however, a decision tree may have leaf nodes which consist of only a few or noise data. The decisions made by those weak leaves will not be effective and therefore should be excluded in the decision process. This paper proposes a method using a classifier, UChoo, for solving a classification problem, and suggests an effective method of decision process involving only the important leaves and thereby excluding the noisy leaves. The experiment shows that this method is effective and reduces the erroneous decisions and can be applied when only important decisions should be made.
- KSP 제안 키워드
- Available data, Classification problems, Data mining(DM), Data sets, Decision Tree(DT), Machine learning system, Performance measurement, Ubiquitous environments, decision process, decision tree algorithm