%0 Journal Article
%T Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization
%A S. Sakinah S. Ahmad
%A Witold Pedrycz
%J Applied Computational Intelligence and Soft Computing
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/347157
%X The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented. 1. Introduction In fuzzy modeling, the two main approaches for generating the rules rely on knowledge acquisition from human experts and knowledge discovery from data [1, 2]. In recent years, knowledge discovery from data or data-driven fuzzy modeling has become more important [2¨C4]. In many cases, the ability to develop models efficiently is hampered by the dimensionality of the input space as well as the number of data. If we are concerned with rule-based models, the high-dimensionality of the feature space along with the topology of the rules gives rise to the curse of dimensionality [1, 4]. The number of rules increases exponentially and is equal to , where is the number of features and stands for the number of fuzzy sets defined for each feature. The factors that contribute most to the accuracy of the data-driven fuzzy modeling are associated with the size of the input space and the decomposition of the input data. A Large number of data points or instances in a continuous input-output domain exhibit a significant impact on fuzzy models. It is well known that more training data will not always lead to a better performance for data-driven models. Large amount of training data have important implications on the modeling capabilities. Since the number of
%U http://www.hindawi.com/journals/acisc/2012/347157/