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Case-Based Reasoning systems retrieve cases using a similarity function based on the K-NN or some derivatives. These functions are sensitive to irrelevant, interacting or noisy features. Many similarity functions weigh the relevance of features to avoid this problem. This article proposes two weighting methods based on Rough Sets theory: Proportional Rough Sets and Dependence Rough Sets. Both weighting methods use the representative knowledge extracted from the original data to compute thedoi:10.4114/ia.v6i15.753 fatcat:l7ng4oxw4nbc7lz54pfz3yowka