A new similarity measure in formal concept analysis for case-based reasoning

Authors:
Jirapond Tadrat;Veera Boonjing;Puntip Pattaraintakorn
Affiliations:
Software Systems Engineering Laboratory, Department of Computer Science, Faculty of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand and National Centre of Excel ...;Software Systems Engineering Laboratory, Department of Computer Science, Faculty of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand and National Centre of Excel ...;Department of Mathematics, Faculty of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand and Faculty of Science and Engineering, York University, Canada
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 17
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Abstract

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.