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The construction of efficient and effective decision trees remains a keytopic in machine learning because of their simplicity and flexibility. A lot ofheuristic algorithms have been proposed to construct near-optimal decisiontrees. ID3, C4.5 and CART are classical decision tree algorithms and the splitcriteria they used are Shannon entropy, Gain Ratio and Gini index respectively.All the split criteria seem to be independent, actually, they can be unified ina Tsallis entropy framework. Tsallis entropy is a generalization of Shannonentropy and provides a new approach to enhance decision trees' performance withan adjustable parameter $q$. In this paper, a Tsallis Entropy Criterion (TEC)algorithm is proposed to unify Shannon entropy, Gain Ratio and Gini index,which generalizes the split criteria of decision trees. More importantly, wereveal the relations between Tsallis entropy with different $q$ and other splitcriteria. Experimental results on UCI data sets indicate that the TEC algorithmachieves statistically significant improvement over the classical algorithms.

Unifying Decision Trees Split Criteria Using Tsallis Entropy