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http://dx.doi.org/10.3745/KIPSTD.2003.10D.1.023

Decision Tree Classifier for Multiple Abstraction Levels of Data  

Jeong, Min-A (광주과학기술원 정보통신학과)
Lee, Do-Heon (한국과학기술원 바이오시스템학과)
Publication Information
The KIPS Transactions:PartD / v.10D, no.1, 2003 , pp. 23-32 More about this Journal
Abstract
Since the data is collected from disparate sources in many actual data mining environments, it is common to have data values in different abstraction levels. This paper shows that such multiple abstraction levels of data can cause undesirable effects in decision tree classification. After explaining that equalizing abstraction levels by force cannot provide satisfactory solutions of this problem, it presents a method to utilize the data as it is. The proposed method accommodates the generalization/specialization relationship between data values in both of the construction and the class assignment phase of decision tree classification. The experimental results show that the proposed method reduces classification error rates significantly when multiple abstraction levels of data are involved.
Keywords
Data Mining; Decision Tree; Abstraction Level; Data Quality; Classification;
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