A Comparative Study of FCA-Based Supervised Classification Algorithms

  • Huaiyu Fu
  • Huaiguo Fu
  • Patrik Njiwoua
  • Engelbert Mephu Nguifo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2961)


Several FCA-based classification algorithms have been proposed, such as GRAND, LEGAL, GALOIS, RULEARNER, CIBLe, and CLNN & CLNB. These classifiers have been compared to standard classification algorithms such as C4.5, Naïve Bayes or IB1. They have never been compared each other in the same platform, except between LEGAL and CIBLe. Here we compare them together both theoretically and experimentally, and also with the standard machine learning algorithm C4.5. Experimental results are discussed.


Class Label Majority Vote Concept Lattice Formal Concept Analysis Concept Selection 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Huaiyu Fu
    • 1
  • Huaiguo Fu
    • 1
  • Patrik Njiwoua
    • 1
  • Engelbert Mephu Nguifo
    • 1
  1. 1.CRIL-CNRS FRE2499Université d’Artois – IUT de LensLens cedexFrance

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