Separability of split value criterion with weighted separation gains

Authors:
Krzysztof Grabczewski
Affiliations:
Department of Informatics, Nicolaus Copernicus University, Toruń, Poland
Venue:
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Year:
2011

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Abstract

An analysis of the Separability of Split Value criterion in some particular applications has led to conclusions about possible improvements of the criterion. Here, the new formulation of the SSV criterion is presented and examined. The results obtained for 21 different benchmark datasets are presented and discussed in comparison with the most popular decision tree node splitting criteria like information gain and Gini index. Because the new SSV definition introduces a parameter, some empirical analysis of the new parameter is presented. The new criterion turned out to be very successful in decision tree induction processes.