Decision Tree Classifier for Multiple Abstraction Levels of Data


The KIPS Transactions:PartD, Vol. 10, No. 1, pp. 23-32, Feb. 2003
10.3745/KIPSTD.2003.10.1.23,   PDF Download:

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.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
M. N. Jeong and D. H. Lee, "Decision Tree Classifier for Multiple Abstraction Levels of Data," The KIPS Transactions:PartD, vol. 10, no. 1, pp. 23-32, 2003. DOI: 10.3745/KIPSTD.2003.10.1.23.

[ACM Style]
Mi Na Jeong and Do Heon Lee. 2003. Decision Tree Classifier for Multiple Abstraction Levels of Data. The KIPS Transactions:PartD, 10, 1, (2003), 23-32. DOI: 10.3745/KIPSTD.2003.10.1.23.