Please use this identifier to cite or link to this item: https://doi.org/10.1080/09528130210164206
Title: Concept lattice based composite classifiers for high predictability
Authors: Xie, Z. 
Hsu, W. 
Liu, Z.
Lee, M.L. 
Keywords: Classification
Concept lattice
Naïve Bayes
Nearest neighbour algorithm
Issue Date: 2002
Source: Xie, Z., Hsu, W., Liu, Z., Lee, M.L. (2002). Concept lattice based composite classifiers for high predictability. Journal of Experimental and Theoretical Artificial Intelligence 14 (2-3) : 143-156. ScholarBank@NUS Repository. https://doi.org/10.1080/09528130210164206
Abstract: Concept lattice model, the core structure in formal concept analysis, has been successfully applied in software engineering and knowledge discovery. This paper integrates the simple base classifier (Naïve Bayes or Nearest Neighbour) into each node of the concept lattice to form a new composite classifier. Two new classification systems are developed, CLNB and CLNN, which employ efficient constraints to search for interesting patterns and voting strategy to classify a new object. CLNB integrates the Naïve Bayes base classifier into concept nodes while CLNN incorporates the Nearest Neighbour base classifier into concept nodes. Experimental results indicate that these two composite classifiers greatly improve the accuracy of their corresponding base classifier. In addition, CLNB even outperforms three other state-of-the-art classification methods, NBTree, CBA and C4.5 Rules.
Source Title: Journal of Experimental and Theoretical Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/39007
ISSN: 0952813X
DOI: 10.1080/09528130210164206
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