The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy\r\nmodels in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization\r\ndeserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in\r\nthe context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection\r\n(namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to\r\nbecome efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use\r\nof advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization\r\nvehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the\r\nsearch space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism\r\nof forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets\r\nis presented.
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