Interpretability indexes for Fuzzy classification in cognitive systems

Classification systems based on Fuzzy Logic are of particular importance in the ambit of cognitive systems, due to their ability of managing uncertainty and presenting interpretable knowledge bases by emulating human cognition processes. However, the notion of interpretability is not yet exhaustively defined. In this work, the properties assessing the interpretability of a fuzzy classifier are discussed, and on this basis two indexes are proposed, which numerically evaluate readability and semantic interpretability, respectively. These indexes are calculated for a benchmark dataset, showing how they can be used to quantitatively compare systems with different characteristics.