Improving fuzzy rule classifier by extracting suitable features from capacities with respect to the choquet integral

In this paper, an iterative method to select suitable features in an industrial pattern recognition context is proposed. It combines a global method of feature selection and a fuzzy linguistic rule classifier. It is applied to an industrial fabric textile context. The aim of the global vision system is to identify textile fabric defects. From the related industrial process, the training data sets are small, and some are incomplete. Moreover, the recognition step must be compatible with the time constant of the system, which generally imposes low complexity for the system. The choice of the most relevant features and the reduction of their number are important to respect these constraints. The feature selection method is based on the analysis of indexes extracted on the lattice defined from training in relation with the Choquet integral. This selection step is embedded in an iterative algorithm to discard weaker features in order to decrease the number of rules while keeping good recognition rates. The recognition step is done with a fuzzy reasoning classifier that is well adapted for this application case. The proposed method is quite efficient with small learning data sets because of the generalization capacity of both the feature selection and recognition steps. The experimental study shows the wanted behavior of this approach: the feature number decreases, whereas the recognition rate increases. Thus, the total number of generated fuzzy rules is reduced.