Case Generation: A Rough-fuzzy Approach

In this article we propose a rough-fuzzy hybridization scheme for case generation. Fuzzy set theory is used for linguistic representation of patterns, thereby producing a granulation of the feature space. Rough set theory is used to obtain dependency rules which model informative regions in the granulated feature space. The fuzzy membership functions corresponding to the informative regions are stored as cases. Case retrieval is made using a fuzzy similarity function. Unlike existing case selection methods, the cases here are cluster granules, and not sample points. Also, the cases involve reduced number of relevant features with variable size. The algorithm is suitable for mining data sets, large both in dimension and size, due to its low time requirement in case generation as well as retrieval. Superiority of the algorithm in terms of classification accuracy, and case generation and retrieval time is demonstrated on some real life datasets.