Bayesian Feature Construction

  • Manolis Maragoudakis
  • Nikos Fakotakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3955)


The present paper discusses the issue of enhancing classification performance by means other than improving the ability of certain Machine Learning algorithms to construct a precise classification model. On the contrary, we approach this significant problem from the scope of an extended coding of training data. More specifically, our method attempts to generate more features in order to reveal the hidden aspects of the domain, modeled by the available training examples. We propose a novel feature construction algorithm, based on the ability of Bayesian networks to represent the conditional independence assumptions of a set of features, thus projecting relational attributes which are not always obvious to a classifier when presented in their original format. The augmented set of features results in a significant increase in terms of classification performance, a fact that is depicted to a plethora of machine learning domains (i.e. data sets from the UCI ML repository and the Artificial Intelligence group) using a variety of classifiers, based on different theoretical backgrounds.


Bayesian Network Name Entity Recognition Bayesian Belief Network Feature Construction Language Technology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Aha, D., Kibler, D., Albert, M.K.: Instance based learning algorithms. Machine Learning 6(1), 37–66 (1991)Google Scholar
  2. 2.
    Cooper, G., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992)zbMATHGoogle Scholar
  3. 3.
    Jensen, R.: An Introduction to Bayesian Networks. UCL Press, London (1996)Google Scholar
  4. 4.
    John, G., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345 (1995)Google Scholar
  5. 5.
    Kavallieratou, E.: Σύστ ημ α Aυτόμα τη ς Eπε ξε ργ ασίας Eγγ ράφο υ κα ι Aνα γνώρι ση ς Xει ρόγρ αφ ων Xαρ ακ τήρω ν Συν εχόμε νη ς Γρα φής, Aνε ξάρτ ητ ο Συγ γρ αφέα, PhD Thesis (2000).Google Scholar
  6. 6.
    Kohavi, R., Dan, S.: Feature subset selection using the wrapper model: Overfitting and dynamic search space topology. In: Fayyad, U.M., Uthurusamy, R. (eds.) First International Conference on Knowledge, Discovery and Data Mining (1995)Google Scholar
  7. 7.
    Lam, W., Bacchus, R.: Learning Bayesian belief networks: An approach based on the MDL principle. Computational Intelligence 10(4), 269–293 (1994)CrossRefGoogle Scholar
  8. 8.
    Markovich, S., Rosenstein, D.: Feature Generation Using General Constructor Functions. Machine Learning 49(1), 59–98 (2002)CrossRefzbMATHGoogle Scholar
  9. 9.
    Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases. [Machine-readable data repository]. University of California, Department of Information and Computer Science, Irvine, CA (1993)Google Scholar
  10. 10.
    Murphy, P.M., Pazzani, M.J.: Exploring the decision forest: An empirical investigation of Occam’s razor in decision tree induction. Journal of Artificial Intelligence Research 1, 257–275 (1994)zbMATHGoogle Scholar
  11. 11.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA (1988)zbMATHGoogle Scholar
  12. 12.
    Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1980); Reiter, R.: A logic for default reasoning. Artificial Intelligence 13(1-2), 81–132 (1993)Google Scholar
  13. 13.
    Salzberg, S.: Improving classification methods via feature selection. Machine Learning 99 (1993)Google Scholar
  14. 14.
    Tasikas, A.: Aνα γνώρι ση Oνο μάτω ν Oντ οτήτω ν σε Kείμε ναας λλ ην ικής Γλώσσ ας απ οκ λε ισ τι κά με Mηχ αν ική Mάθη σ, Diploma Thesis, University of Patras (2002)Google Scholar
  15. 15.
    Zervas, P., Maragoudakis, M., Fakotakis, N., Kokkinakis, G.: Learning to predict Pitch Accents using Bayesian Belief Networks for Greek Language. In: LREC 2004, 4th International Conference on Language Resources and Evaluation, Lisbon, Portugal, pp. 2139–2142 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Manolis Maragoudakis
    • 1
  • Nikos Fakotakis
    • 1
  1. 1.Artificial Intelligence GroupUniversity of PatrasRion, PatrasGreece

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