New results on minimum error entropy decision trees

Authors:
J. P. Marques de Sá;Raquel Sebastião;João Gama;Tânia Fontes
Affiliations:
INEB-Instituto de Engenharia Biomédica, FEUP, Universidade do Porto, Porto, Portugal;LIAAD - INESC Porto, L.A., Porto, Portugal;LIAAD - INESC Porto, L.A., Porto, Portugal;INEB-Instituto de Engenharia Biomédica, FEUP, Universidade do Porto, Porto, Portugal
Venue:
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2011

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Abstract

We present new results on the performance of Minimum Error Entropy (MEE) decision trees, which use a novel node split criterion. The results were obtained in a comparive study with popular alternative algorithms, on 42 real world datasets. Carefull validation and statistical methods were used. The evidence gathered from this body of results show that the error performance of MEE trees compares well with alternative algorithms. An important aspect to emphasize is that MEE trees generalize better on average without sacrifing error performance.