Measuring interpretability in rule-based classification systems

The "unique selling point" of fuzzy systems is usually the interpretability of its rule base. However, very often only the accuracy of the rule base is measured and used to compare a fuzzy system to other solutions. We have suggested an index to measure the interpretability of fuzzy rule bases for classification problems. However, the index can be used to describe the interpretability of any rule-based system that uses sets to partition variables. We demonstrate the features of the index by using two data sets, one simple benchmark set and a real-world example.