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terms of the classi cation accuracy obtained using the 1-NN rule. Comparisons are made with the conventional IB3 algorithm [14] and random case selection method. This methodology is found to perform better in terms of 1-NN accuracy, as well as improving case generation time and average case retrieval time. In the following sections we describe the methodology for fuzzy linguistic representation of objects. Then we present the methodology used to obtain dependency rules and the algorithm for mapping dependency rules to cases. The case retrieval mechanism is then described. Finally, some experimental results and comparisons with other approaches are presented. 2.3.3.1 Linguistic (Fuzzy) Representation of Patterns As mentioned earlier, rough set theory deals with a set of objects in a granular universe. Here we describe a way of obtaining the granular feature space using fuzzy linguistic representation of patterns. Only the case of numeric features is mentioned here. (Features in descriptive and set forms can also be handled in this framework.) Details of the methodologies involved may be found in [12,13]. Let a pattern (object) ^ be represented by n numeric features (attributes) (i.e., e e [F1, F2, . . ., Fn]). Each feature is described in terms of its fuzzy membership values, corresponding to three linguistic fuzzy sets: low (L), medium (M), and high (H). Thus, an n-dimensional pattern vector is represented as a 3n-dimensional vector [12,13]: ^ mlow F1 ^ ; mmedium F1 ^ ; mhigh F1 ^ ; mlow F2 ^ ; mmedium F2 ^ ; mhigh F2 ^ ; . . .; e e e e e e e e e e mlow Fn ^ ; mmedium Fn ^ ; mhigh Fn ^ 2:11

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