An Experimental Comparison of Old and New Decision Tree Algorithms

This paper presents a detailed comparison of a recently proposed algorithm for optimizing decision trees, tree alternating optimization (TAO), with other popular, established algorithms, such as CART and C5.0. We compare their performance on a number of datasets of different size, dimensionality and number of classes, across different performance factors: accuracy and tree size (in terms of the number of leaves or the depth of the tree). We find that TAO achieves higher accuracy in every single dataset, often by a large margin.