An Evaluation of Rule-Based Classification Models Induced by a Fuzzy Method and Two Classic Learning Algorithms

Classification is a widely researched area in the machine learning and fuzzy communities with several approaches proposed by both communities. Some of the most relevant rule-based approaches from the machine learning community might include decision trees and rule inducers. The fuzzy community has also proposed many rule-based approaches, such as fuzzy decision trees and genetic fuzzy systems. This paper aims at comparing the models generated by rule-based methods for classification from both communities in terms of accuracy, and the induced rule set in terms of the syntactic complexity, taking into account the number of rules and average number of conjunctions in those rules. In general, models with lower syntactic complexity also show better interpretability, which is an important issue in knowledge acquisition. Results, using 10 datasets, a fuzzy C4.5 algorithm and two classic machine learning algorithms (C4.5 and PART), show that the fuzzy approach is able to produce lower error rates. Regarding the syntactic complexity of the models, PART produces in most cases the simplest models, although learning from different sets of features selected by filters. However, these simple models do not necessarily show a low error rate. Nevertheless, the induced fuzzy models inherit, from the fuzzy logic, the embedded ability of processing uncertainty and imprecision, avoiding the creation of rules using unnatural divisions of the attributes as the classic algorithms might do.