Abstract
To build competent and efficient CBR classifiers, we develop a case generation approach which integrates fuzzy sets, rough sets and learning vector quantization (LVQ). If the feature values of the cases are numerical, fuzzy sets are firstly used to discretize the feature spaces. Secondly, a fast rough set-based feature selection method is built to identify the significant features. The representative cases (prototypes) are then generated through LVQ learning process on the case bases after feature selection. These prototypes can be also considered as the extracted knowledge which improves the understanding of the case base. Three real life data sets are used in the experiments to demonstrate the effectiveness of this case generation approach.
This work is supported by the Hong Kong government CERG research grant BQ-496.
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Li, Y., Shiu, S.CK., Pal, S.K., Liu, J.NK. (2005). Rough Learning Vector Quantization Case Generation for CBR Classifiers. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_14
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DOI: https://doi.org/10.1007/11548706_14
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