Comparison of greedy algorithms for α-decision tree construction

Authors:
Abdulaziz Alkhalid;Igor Chikalov;Mikhail Moshkov
Affiliations:
Mathematical and Computer Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia;Mathematical and Computer Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia;Mathematical and Computer Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Venue:
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Year:
2011

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Abstract

A comparison among different heuristics that are used by greedy algorithms which constructs approximate decision trees (α-decision trees) is presented. The comparison is conducted using decision tables based on 24 data sets from UCI Machine Learning Repository [2]. Complexity of decision trees is estimated relative to several cost functions: depth, average depth, number of nodes, number of nonterminal nodes, and number of terminal nodes. Costs of trees built by greedy algorithms are compared with minimum costs calculated by an algorithm based on dynamic programming. The results of experiments assign to each cost function a set of potentially good heuristics that minimize it.