A New Aggregated Attribute Values Match Technique for Improving the Quality of Probability Estimated Decision Trees

Abstract

Probability estimations of decision trees may not be useful directly because their poor probability estimations but the best probability estimations are desired in many useful applications. Many techniques have been proposed for obtaining good probability estimations of decision trees. Two such optical techniques are identified and the first one is single tree based aggregation of mismatched attribute values of instances. The second one is bagging technique but it is costly and less comprehensible. So, in this paper a single aggregated probability estimation decision tree model technique is proposed for improving the performance of probability estimations of decision trees and the performance of new technique is evaluated using area under the curve (AUC) evaluation technique. The proposed technique computes aggregate scores based on matched attribute values of test tuples.