2024-03-29T07:49:59Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1344472024-03-13T09:53:00Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134243
00925njm 22002777a 4500
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Fernández Riverola, Florentino
author
Díaz, Fernando
author
Corchado Rodríguez, Juan Manuel
author
2007
Early work on case-based reasoning (CBR) reported in the literature shows the importance of soft computing techniques applied to different stages of the classical four-step CBR life cycle. This correspondence proposes a reduction technique based on rough sets theory capable of minimizing the case memory by analyzing the contribution of each case feature. Inspired by the application of the minimum description length principle, the method uses the granularity of the original data to compute the relevance of each attribute. The rough feature weighting and selection method is applied as a preprocessing step prior to the generation of a fuzzy rule system, which is employed in the revision phase of the proposed CBR system. Experiments using real oceanographic data show that the rough sets reduction method maintains the accuracy of the employed fuzzy rules, while reducing the computational effort needed in its generation and increasing the explanatory strength of the fuzzy rules.
IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews). Volumen 37 (1), pp. 138-146. Institute of Electrical & Electronics Engineers (IEEE).
1094-6977 (Print)
http://hdl.handle.net/10366/134447
Computer Science
Reducing the Memory Size of a Fuzzy Case-Based Reasoning System Applying Rough Set Techniques