T3C: improving a decision tree classification algorithm’s interval splits on continuous attributes

Panagiotis Tzirakis and Christos Tjortjis ()
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Panagiotis Tzirakis: University of Crete
Christos Tjortjis: International Hellenic University

Advances in Data Analysis and Classification, 2017, vol. 11, issue 2, No 7, 353-370

Abstract: Abstract This paper proposes, describes and evaluates T3C, a classification algorithm that builds decision trees of depth at most three, and results in high accuracy whilst keeping the size of the tree reasonably small. T3C is an improvement over algorithm T3 in the way it performs splits on continuous attributes. When run against publicly available data sets, T3C achieved lower generalisation error than T3 and the popular C4.5, and competitive results compared to Random Forest and Rotation Forest.

Keywords: Data mining; Classification; Decision trees; Interval splits; 68T05 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11634-016-0246-x

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