A Heuristic Approach to Learning Rules from Fuzzy Databases
José Ranilla, Luis José Rodríguez-Muñiz · IEEE Intelligent Systems · 2007
As an alternative to approaches based on entropy and information gain, we describe a system that uses a measure called the impurity level. The learning algorithm based on this measure, which we call FARNI, first induces fuzzy decision trees by using an impurity-level extension for selecting the best branch. This is similar to the way C4.5 and ARNI induce selections for crisp databases. Once FARNI calculates the fuzzy decision tree, it returns compact fuzzy rule sets that apply a pruning process