Advances in Knowledge Discovery and Data Mining

Volume 3918 of the series Lecture Notes in Computer Science pp 35-44

Generalized Conditional Entropy and a Metric Splitting Criterion for Decision Trees

  • Dan A. SimoviciAffiliated withDept. of Computer Science, University of Massachusetts at Boston
  • , Szymon JaroszewiczAffiliated withFaculty of Computer and Information Systems, Technical University of Szeczin

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We examine a new approach to building decision tree by introducing a geometric splitting criterion, based on the properties of a family of metrics on the space of partitions of a finite set. This criterion can be adapted to the characteristics of the data sets and the needs of the users and yields decision trees that have smaller sizes and fewer leaves than the trees built with standard methods and have comparable or better accuracy.


decision tree generalized conditional entropy metric metric betweenness