A New Aggregated Attribute Values Match Technique for Improving the Quality of Probability Estimated Decision Trees
D. Mabuni, Department of Computer Science, Dravidian University, Kuppam, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 30, 2020. | Manuscript published on May 10, 2020. | PP: 446-452 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5323059720/2020©BEIESP | DOI: 10.35940/ijitee.G5323.059720
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Aggregate scores, bagging technique, mismatched and matched attribute values, poor probability estimations.
Scope of the Article: Data Mining Methods, Techniques, and Tools