Lazy Classification Using an Optimized Instance-Based Learner

  • Rui Pedro Barbosa
  • Orlando Belo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)


Classification is a machine learning technique whose objective is the prediction of the class membership of data instances. There are numerous models currently available for performing classification, among which decision trees and artificial neural networks. In this article we describe the implementation of a new lazy classification model called similarity classifier. Given an out-of-sample instance, this model predicts its class by finding the training instances that are similar to it, and returning the most frequent class among these instances. The classifier was implemented using Weka’s data mining API, and is available for download. Its performance, according to accuracy and speed metrics, compares relatively well with that of well-established classifiers such as nearest neighbor models or support vector machines. For this reason, the similarity classifier can become a useful instrument in a data mining practitioner’s tool set.


Data mining supervised learning classification 


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  1. 1.
    Rhodes, D.R., Yu, J., Shanker, K., Deshpande, N., Varambally, R., Ghosh, D., Barrette, T., Pandey, A., Chinnaiyan, A.M.: ONCOMINE: A Cancer Microarray Database and Integrated Data-Mining Platform. Neoplasia 6(1), 1–6 (2004)CrossRefGoogle Scholar
  2. 2.
    Pantel, P., Lin, D.: SpamCop: A Spam Classification & Organization Program. In: Learning for Text Categorization: Papers from the 1998 Workshop, pp. 95–98 (1998)Google Scholar
  3. 3.
    John, G.H., Langley, P.: Estimating Continuous Distributions in Bayesian Classifiers. In: 11th Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann, San Francisco (1995)Google Scholar
  4. 4.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-Based Learning Algorithms. Machine Learning 6(1), 37–66 (1991)Google Scholar
  5. 5.
    Cleary, J.G., Trigg, L.E.: K*: An Instance-Based Learner Using an Entropic Distance Measure. In: 12th International Conference on Machine Learning, pp. 108–114. Morgan Kaufmann, San Francisco (1995)Google Scholar
  6. 6.
    Fayyad, U.M., Irani, K.B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In: 13th International Joint Conference on Artificial Intelligence, pp. 1022–1027. Morgan Kaufmann, San Francisco (1993)Google Scholar
  7. 7.
    Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  8. 8.
    UCI Machine Learning Repository,

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rui Pedro Barbosa
    • 1
  • Orlando Belo
    • 1
  1. 1.Department of InformaticsUniversity of MinhoPortugal

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