We propose a method for learning rule-based classifiers that can be seen as a compromise
between a complete enumeration of the hypothesis space and heuristic strategies to search …

The integration of supervised classification and association rules for building classification
models is not new. One major advantage is that models are human readable and can be …

We study a new method for improving the classification accuracy of a model composed of
classification association rules (CAR). The method consists in reordering the original set of …

Classification is an important field of data mining problems. Given a set of labeled training
examples the classification task constructs a classifier. A classifier is a global model which is …

Conventional rule learning algorithms aim at finding a set of simple rules, where each rule
covers as many examples as possible. In this paper, we argue that the rules found in this …

Learning classifier systems use genetic algorithms to facilitate rule-discovery, where rule
fitness has traditionally been payoff prediction-based. Current research has shifted to the …

In this paper, starting from a collection of training examples, we show how to produce a very
compact set of classification rules. The induction idea is a clustering principle based on …

A system of rule extraction out of a complex hierarchical classifier is proposed in this paper.
There are several methods for rule extraction out of trained artificial neural networks (ANN's) …

In this paper we study a new technique we call post-bagging, which consists in resampling
parts of a classification model rather then the data. We do this with a particular kind of model …

Several rule induction schemes generate hypotheses in the form of unordered rule sets. One
important problem that has to be addressed when classifying examples with such …