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Author: search.filters.author.Boonjing, VeeraSubject: search.filters.subject.Case-based reasoning

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    A Knowledge Integrated Case-Based Classifier
    (2019-06-01)
    Muangprathub, Jirapond
    ;
    Kajornkasirat, Siriwan
    ;
    Wanichsombat, Apirat
    ;
    Boonjing, Veera
    ;
    Saelee, Jarunee
    This paper proposes a case-based classifier using a new approach that integrates rule-based and case-based reasoning approaches for enhanced accuracy. The rule-based reasoning component uses rules generated from a concept lattice of training data, binarized using fuzzy sets. These binarized data are stored as cases in the case-based classification component. The case-based component complements the rule-based component to enhance classification accuracy. Moreover, we designed the case-based component with an embedded similarity measure that uses a vector model for concept approximations. Thus, this design makes it possible to generate high quality rules and classify unseen new cases. In addition, the ability to build a knowledge base in lattice form is important for discovering hierarchical patterns, incrementing or updating the existing knowledge base, and inducing rules with our rule learning algorithm. The novel methodology was implemented and evaluated with benchmark datasets from the UCI repository and historic rubber prices in Thailand, demonstrating improvements in accuracy of classification calls. The results from the fact their several hierarchical datasets are very promising, with improved classification performance over prior reported methods.
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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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    A new similarity measure in formal concept analysis for case-based reasoning
    (2012-01-01)
    Tadrat, Jirapond
    ;
    Boonjing, Veera
    ;
    Pattaraintakorn, Puntip
    In this work, we aim at developing a better knowledge base by using formal concept analysis (FCA) and propose its new similarity measure based on vector model for case-based reasoning (CBR). The features of our proposed approaches are illustrated using a part of CBR system for both classification and problem-solving. Concept lattice knowledge base provides more accuracy classification for hierarchical data structure when comparing with non-hierarchical data structure. Dependency induced from our concept lattice knowledge base can help to suggest informative solutions for problem-solving CBR. In addition, our similarity measure improves the accuracy of classification CBR significantly when we perform experiments on the UCI data sets with cross validation. © 2011 Elsevier Ltd. All rights reserved.