The article describes a method combining two widely-used empirical approaches to learning from examples: rule induction and instance-based learning. In our algorithm (RIONA) decision is predicted not on the basis of the whole support set of all rules matching a test case, but the support set restricted to a neighbourhood of a test case. The size of the optimal neighbourhood is automatically induced during the learning phase. The empirical study shows the interesting fact that it is enough to consider a small neighbourhood to achieve classification accuracy comparable to an algorithm considering the whole learning set. The combination of k-NN and a rule-based algorithm results in a significant acceleration of the algorithm using all minimal rules. Moreover, the presented classifier has high accuracy for both kinds of domains: more suitable for k-NN classifiers and more suitable for rule based classifiers.

The article describes a method combining two widely-used empirical approaches: rule induction and instance-based learning. In our algorithm (RIONA) decision is predicted not on the basis of the whole support set of all rules matching a test case, but the support set restricted to a neighbourhood of a test case. The size of the optimal neighbourhood is automatically induced during the learning phase. The empirical study shows the interesting fact that it is enough to consider a small neighbourhood to preserve classification accuracy. The combination of k-NN and a rule-based algorithm results in a significant acceleration of the algorithm using all minimal rules. We study the significance of different components of the presented method and compare its accuracy to well-known methods.

We propose a novel lazy learning method called QPAL. QPAL does not simply utilize a kind of distance measure between the query instance and training instances as many lazy learning methods do. It attempts to discover useful patterns known as query projections, which are customized to the query instance. The discovery for useful QPs is conducted in an innovative way. QPAL can guarantee to discover high-quality QPs in the learning process. We use some benchmark data sets and a spam email filtering problem to evaluate QPAL and demonstrate that QPAL achieves good performance and high reliability. 1