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Credal Decision Trees to Classify Noisy Data Sets

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Hybrid Artificial Intelligence Systems (HAIS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8480))

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Abstract

Credal Decision Trees (CDTs) are algorithms to design classifiers based on imprecise probabilities and uncertainty measures. C4.5 and CDT procedures are combined in this paper. The new algorithm builds trees for solving classification problems assuming that the training set is not fully reliable. This algorithm is especially suitable to classify noisy data sets. This is shown in the experiments.

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References

  1. Abellán, J., Moral, S.: Building classification trees using the total uncertainty criterion. International Journal of Intelligent Systems 18(12), 1215–1225 (2003)

    Article  MATH  Google Scholar 

  2. Abellán, J.: Uncertainty measures on probability intervals from Imprecise Dirichlet model. International Journal of General Systems 35(5), 509–528 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  3. Abellán, J., Masegosa, A.: Bagging schemes on the presence of noise in classification. Expert Systems with Applications 39(8), 6827–6837 (2012)

    Article  Google Scholar 

  4. Demsar, J.: Statistical Comparison of Classifiers over Multiple Data Sets. Journal of Machine Learning Research 7, 1–30 (2006)

    MATH  MathSciNet  Google Scholar 

  5. Klir, G.J.: Uncertainty and Information, Foundations of Generalized Information Theory. John Wiley, Hoboken (2006)

    MATH  Google Scholar 

  6. Mantas, C.J., Abellán, J.: Analysis and extension of decision trees based on imprecise probabilities: application on noisy data. Expert Systems with Applications 41, 2514–2525 (2014)

    Article  Google Scholar 

  7. Quinlan, J.R.: Programs for Machine Learning. Morgan Kaufmann series in Machine Learning (1993)

    Google Scholar 

  8. Shannon, C.E.: A mathematical theory of communication. The Bell System Technical Journal 423, 379–423, 623–656 (1948)

    Google Scholar 

  9. Walley, P.: Inferences from multinomial data, learning about a bag of marbles. Journal of the Royal Statistical Society, Series B 58, 3–57 (1996)

    MATH  MathSciNet  Google Scholar 

  10. Witten, I.H., Frank, E.: Data Mining, Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Carlos J. Mantas & Joaquín Abellán

Authors
  1. Carlos J. Mantas
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  2. Joaquín Abellán
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Avenue, 1678, Nicosia, Cyprus

    Marios Polycarpou

  2. Department of Computer Science, University of Sao Paulo at Sao Carlos, Sao Carlos, SP, Brazil

    André C. P. L. F. de Carvalho

  3. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, 80778, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  4. Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  5. University of Salamanca, Plaza de la Merced S/N, 37008 Salamanca, Spain and University of A Coruna, Escuela Universitaria Politecnica, Departamento de Enxeñeria Industrial, A Coruna, Spain

    Héctor Quintian

  6. University of Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado

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© 2014 Springer International Publishing Switzerland

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Mantas, C.J., Abellán, J. (2014). Credal Decision Trees to Classify Noisy Data Sets. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_60

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  • DOI: https://doi.org/10.1007/978-3-319-07617-1_60

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07616-4

  • Online ISBN: 978-3-319-07617-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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