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An ensemble method using credal decision trees

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  • Abellán, Joaquín
  • Masegosa, Andrés R.

Abstract

Supervised classification learning can be considered as an important tool for decision support. In this paper, we present a method for supervised classification learning, which ensembles decision trees obtained via convex sets of probability distributions (also called credal sets) and uncertainty measures. Our method forces the use of different decision trees and it has mainly the following characteristics: it obtains a good percentage of correct classifications and an improvement in time of processing compared with known classification methods; it not needs to fix the number of decision trees to be used; and it can be parallelized to apply it on very large data sets.

Suggested Citation

  • Abellán, Joaquín & Masegosa, Andrés R., 2010. "An ensemble method using credal decision trees," European Journal of Operational Research, Elsevier, vol. 205(1), pages 218-226, August.
    • Handle: RePEc:eee:ejores:v:205:y:2010:i:1:p:218-226
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    References listed on IDEAS

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    1. van Wezel, Michiel & Potharst, Rob, 2007. "Improved customer choice predictions using ensemble methods," European Journal of Operational Research, Elsevier, vol. 181(1), pages 436-452, August.
    2. West, David & Mangiameli, Paul & Rampal, Rohit & West, Vivian, 2005. "Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application," European Journal of Operational Research, Elsevier, vol. 162(2), pages 532-551, April.
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    Cited by:

    1. Azad, Mohammad & Moshkov, Mikhail, 2017. "Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions," European Journal of Operational Research, Elsevier, vol. 263(3), pages 910-921.
    2. Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, vol. 234(1), pages 127-139.
    3. Abellán, Joaquín & Baker, Rebecca M. & Coolen, Frank P.A. & Crossman, Richard J. & Masegosa, Andrés R., 2014. "Classification with decision trees from a nonparametric predictive inference perspective," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 789-802.
    4. Abellán, Joaquín & Baker, Rebecca M. & Coolen, Frank P.A., 2011. "Maximising entropy on the nonparametric predictive inference model for multinomial data," European Journal of Operational Research, Elsevier, vol. 212(1), pages 112-122, July.
    5. Chikalov, Igor & Hussain, Shahid & Moshkov, Mikhail, 2018. "Bi-criteria optimization of decision trees with applications to data analysis," European Journal of Operational Research, Elsevier, vol. 266(2), pages 689-701.
    6. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2012. "A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction," European Journal of Operational Research, Elsevier, vol. 218(1), pages 163-174.

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