How to Compare and Interpret Two Learnt Decision Trees from the Same Domain?

Abstract

Data mining methods are widely used across many disciplines to identify patterns, rules or associations among huge volumes of data. Decision tree induction such as C4.5 is the most preferred method for classification since it works well on average regardless of the data set being used. The resulting decision tree has explanation capability but problems arise if the data set has been collected at different times or is enlarging and the decision tree induction process has been repeated. The resulting tree will change and the expert is questioning the trustworthy of the result. That brings us to the problem of comparing two decision trees in accordance with its explanation power. In this paper, we present a method how to compare two decision trees and how to interpret the change of the structure and the attributes in the decision tree.