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 phases 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.
This work has been supported by Korea Science and Engineering Foundation (KOSEF) through the Advanced Information Technology Research Center (AITrc).
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Lee, D., Jeonga, M., Won, YK. (2001). Decision Trees for Multiple Abstraction Levels of Data. In: Klusch, M., Zambonelli, F. (eds) Cooperative Information Agents V. CIA 2001. Lecture Notes in Computer Science(), vol 2182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44799-7_9
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DOI: https://doi.org/10.1007/3-540-44799-7_9
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