Effects of Antecedent Pruning in Fuzzy Classification Systems

Fuzzy classification rules are widely considered a well-suited representation of classification knowledge, as they allow readable and interpretable rule bases. The goal of this paper is to discuss the shapes of the resulting classification borders under consideration of different types of fuzzy sets, rule bases and t-norms and thus which class distributions can be represented by such classification systems. We focus on discussing how antecedent pruning influences the classification behaviour of fuzzy classifiers. Our main goal is to give the potential user an insight into the classification behaviour of fuzzy classifiers. For this, mainly 2D and 3D visualisations are used to illustrate the cluster shapes and the borders between distinct classes