T3C: Improving a decision tree classification algorithms interval splits on continuous attributes. 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.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Khan, Zardad; Gul, Asma; Perperoglou, Aris; Miftahuddin, Miftahuddin; Mahmoud, Osama; Adler, Werner; Lausen, Berthold: Ensemble of optimal trees, random forest and random projection ensemble classification (2020)
- Bertsimas, Dimitris; Dunn, Jack: Optimal classification trees (2017)
- Tzirakis, Panagiotis; Tjortjis, Christos: T3C: improving a decision tree classification algorithm’s interval splits on continuous attributes (2017)