EXPLORE: a novel decision tree classification algorithm

Authors:
Md Zahidul Islam
Affiliations:
School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, Australia
Venue:
BNCOD'10 Proceedings of the 27th British national conference on Data Security and Security Data
Year:
2010

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Abstract

Decision tree algorithms such as See5 (or C5) are typically used in data mining for classification and prediction purposes. In this study we propose EXPLORE, a novel decision tree algorithm, which is a modification of See5. The modifications are made to improve the capability of a tree in extracting hidden patterns. Justification of the proposed modifications is also presented. We experimentally compare EXPLORE with some existing algorithms such as See5, REPTree and J48 on several issues including quality of extracted rules/patterns, simplicity, and classification accuracy of the trees. Our initial experimental results indicate advantages of EXPLORE over existing algorithms.