In this paper, several demands placed on concept description algorithms are identified and discussed. The most important criterion is the ability to produce compact rule sets that, in a natural and accurate way, describe the most important relationships in the underlying domain. An algorithm based on the identified criteria is presented and evaluated. The algorithm, named Chipper, produces deci...

In this paper, we demonstrate that simple fuzzy rule-based classification systems with high interpretability are obtained through multiobjective genetic rule selection. In our approach, first a prespecified number of candidate fuzzy rules are extracted from numerical data in a heuristic manner using rule evaluation criteria. Then multiobjective genetic rule selection is applied to the extracted...

We consider the concept of a local set of inference rules. A local rule set can be automatically transformed into a rule set for which bottom up evaluation terminates in polynomial time. The local rule set transformation gives polynomial time evaluation strategies for a large variety of rule sets that can not be given terminating evaluation strategies by any other known automatic technique. Thi...

Fuzzy systems built on sparse rule bases apply special inference techniques. A large family of them can be described by the concept of the general methodology of the fuzzy rule interpolation (GM) [1]. Accordingly to this the conclusion is produced in two steps. First a new rule is interpolated corresponding to the position of the reference point of the observation in each antecedent dimension. ...

In this contribution we carry out an analysis of the Fuzzy Reasoning Methods for Fuzzy Rule Based Classification Systems in the framework of balanced and imbalanced data-sets with different degrees of imbalance. We analyze the behaviour of the Fuzzy Rule Based Classification Systems searching for the best type of Fuzzy Reasoning Method in each case, also studying the cooperation of some pre-pro...

The Pittsburgh representation is a well-known encoding for symbolic classifiers in evolutionary algorithms, where each individual represents one symbolic classifier, and each symbolic classifier is composed by a rule set. These rule sets can be interpreted as ordered or unordered sets. The major difference between these two approaches is whether rule ordering defines a rule precedence relations...

This paper proposes a new method for measuring the performance of models—whether decision trees or sets of rules—inferred by machine learning methods. Inspired by the minimum description length (MDL) philosophy and theoretically rooted in information theory, the new method measures the complexity of test data with respect to the model. It has been evaluated on rule sets produced by several diff...