Generating fuzzy rules for high-dimensional data has been a serious challenge in designing fuzzy rule-based classification systems. For data sets with low dimensions, there are some efficient methods to generate a compact set of short fuzzy rules. However, when the dimensions go up, the number of rules increases exponentially. One solution for lowering the dimensions is feature selection which selects a subset of more effective features. In this regard, a fuzzy feature selection approach is proposed in this paper which tries to choose more relevant features; those which can distinguish the distinct classes well. Our method employs the training patterns in the subspace of some predefined fuzzy sets on each feature and applies their compatibility degrees to evaluate that feature. Since each feature is evaluated individually, this method can be applied efficiently on high-dimensional data. Using the selected features to generate rules in fuzzy rule-based classifiers, this paper also presents a novel criterion to assess each generated rule. This criterion measures the capability of each fuzzy rule in discriminating the positive and negative patterns. To illustrate the scalability of our fuzzy feature selection method beside to the efficiency of generated fuzzy rules, they are applied on some benchmark data sets and the results are compared to some other methods in the literature. The experimental results justify the feasibility of our approach to work with high-dimensional data and its acceptable performance in terms of designing CPU time and classification accuracy.
['Eghbal G. Mansoori', 'Khadijeh S. Shafiee']
On fuzzy feature selection in designing fuzzy classifiers for high-dimensional data