A new case-based classification using incremental concept lattice knowledge

  • Authors:
  • Jirapond Muangprathub;Veera Boonjing;Puntip Pattaraintakorn

  • Affiliations:
  • Faculty of Science and Industrial Technology, Prince of Songkla University, Suratthani Campus, Surattani 84000, Thailand;National Centre of Excellence in Mathematics, PERDO, Bangkok 10400, Thailand and Department of Mathematics, Faculty of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Th ...;Faculty of Science and Engineering, York University, Canada

  • Venue:
  • Data & Knowledge Engineering
  • Year:
  • 2013

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Abstract

This paper proposes a new case-based classification system with an incremental knowledge base. The new system employs a concept lattice with formal concept analysis as a knowledge structure. The paper also proposes a new efficient algorithm for knowledge construction as well as an effective retrieval method for formal concepts. The proposed retrieval method uses a concept similarity measure based on an appearance frequency of formal concepts. In addition, we provide a mathematical proof that the similarity measure satisfies a formal similarity metric definition. Experiment results on standard datasets show that our classifier with the proposed similarity measure gives accuracy better than with other existing similarity measures.