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Complexity of the CFP, a method for Classification based on Feature Partitioning

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Advances in Artificial Intelligence (AI*IA 1993)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 728))

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Abstract

This paper presents a new methodology for learning from examples, called Classification by Feature Partitioning (CFP). Learning in CFP is accomplished by storing the objects separately in each feature dimension as disjoint partitions of values. A partition is expanded through generalization or specialized by subdividing it into sub-partitions. It is shown that the CFP algorithm has a low sample and training complexity.

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References

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Authors and Affiliations

  1. Computer Engineering and Information Science Department, Bilkent University, 06533, Ankara, Turkey

    H. Altay Güvenir & İzzet Şirin

Authors
  1. H. Altay Güvenir
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  2. İzzet Şirin
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Pietro Torasso

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© 1993 Springer-Verlag Berlin Heidelberg

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Güvenir, H.A., Şirin, İ. (1993). Complexity of the CFP, a method for Classification based on Feature Partitioning. In: Torasso, P. (eds) Advances in Artificial Intelligence. AI*IA 1993. Lecture Notes in Computer Science, vol 728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57292-9_58

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  • DOI: https://doi.org/10.1007/3-540-57292-9_58

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-57292-3

  • Online ISBN: 978-3-540-48038-9

  • eBook Packages: Springer Book Archive

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