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Subject: search.filters.subject.Concept lattice

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    A new case-based classification using incremental concept lattice knowledge
    (2013-01-01)
    Muangprathub, Jirapond
    ;
    Boonjing, Veera
    ;
    Pattaraintakorn, Puntip
    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. © 2012 Elsevier B.V.
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    Building classification rules for case-based classifier using fuzzy sets and formal concept analysis
    (2008-12-01)
    Tadrat, Jirapond
    ;
    Boonjing, Veera
    ;
    Pattaraintakorn, Puntip
    The focus of this paper is a construction of better knowledge base in case-based classifier system. Our knowledge base structure is based on concept lattice where rules are built from its subconcept-superconcept relation. Since the lattice can only be constructed from inputs with binary attributes, descriptive and numeric attributes must be transformed to binary attributes. In this paper, we propose the transformation of numeric attributes to descriptive attributes using fuzzy set theory. We experiment on benchmark data sets, Car and Iris, to determine the performance in term of number of rules used and classification precision. The results show that trend of accuracy is proportional to the size of learning inputs. The number of rules used is relatively small compared with size of training data. Our case-based classifier produces very promising results in practice and can classify the new problem more accurate than traditional classifiers. Copyright 2008 ACM.
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