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New Results on Minimum Error Entropy Decision Trees

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 7042))

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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.

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Author information

Authors and Affiliations

  1. INEB-Instituto de Engenharia Biomédica, FEUP, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal

    J. P. Marques de Sá & Tânia Fontes

  2. LIAAD - INESC Porto, L.A., Rua de Ceuta, 118, 6, 4050-190, Porto, Portugal

    Raquel Sebastião & João Gama

Authors
  1. J. P. Marques de Sá

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  2. Raquel Sebastião

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  3. João Gama

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  4. Tânia Fontes

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Editor information

Editors and Affiliations

  1. Universidad de La Frontera, Avda. Francisco Salazar, 01145, Temuco, Chile

    César San Martin

  2. Myongji University, San 38-2, Namdong, 449-728, Cheoingu, Yongin, Republic of Korea

    Sang-Woon Kim

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© 2011 Springer-Verlag Berlin Heidelberg

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de Sá, J.P.M., Sebastião, R., Gama, J., Fontes, T. (2011). New Results on Minimum Error Entropy Decision Trees. In: San Martin, C., Kim, SW. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2011. Lecture Notes in Computer Science, vol 7042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25085-9_42

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