Artificial Intelligence Review

, Volume 26, Issue 3, pp 159–190

Machine learning: a review of classification and combining techniques

  • S. B. Kotsiantis
  • I. D. Zaharakis
  • P. E. Pintelas
Article

DOI: 10.1007/s10462-007-9052-3

Cite this article as:
Kotsiantis, S.B., Zaharakis, I.D. & Pintelas, P.E. Artif Intell Rev (2006) 26: 159. doi:10.1007/s10462-007-9052-3

Abstract

Supervised classification is one of the tasks most frequently carried out by so-called Intelligent Systems. Thus, a large number of techniques have been developed based on Artificial Intelligence (Logic-based techniques, Perceptron-based techniques) and Statistics (Bayesian Networks, Instance-based techniques). The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various classification algorithms and the recent attempt for improving classification accuracy—ensembles of classifiers.

Keywords

Classifiers Data mining techniques Intelligent data analysis Learning algorithms 

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • S. B. Kotsiantis
    • 1
    • 2
  • I. D. Zaharakis
    • 3
  • P. E. Pintelas
    • 1
    • 2
  1. 1.Department of Computer Science and TechnologyUniversity of PeloponnesePeloponneseGreece
  2. 2.Educational Software Development Laboratory, Department of MathematicsUniversity of PatrasPatrasGreece
  3. 3.Computer Technology InstitutePatrasGreece

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