Abstract
A machine learning (ML) system which combines lattice-based and instance-based learning (IBL) techniques, is motivated and developed in this paper. We describe an IBL system over lattice theory called IGLUE that significantly improved both the complexity and accuracy of lattice-based learning systems. For this purpose, IGLUE uses the entropy function to select relevant lattice nodes, then extracts a set of new numerical features from the original set of boolean features, and finally applies a nearest neighbor technique with the Mahalanobis distance as the similarity measure between redescribed instances. IGLUE treats only domains described with symbolic features. In this paper, we present results of experiments we carried out to assess how well IGLUE performs on real problems, with other similarity measures and selection functions. We combine three selection functions and three similarity measures, and thus run nine experiments. We compare the performance of these combined strategies on a collection of ML benchmarks. Empirical results indicate that IGLUE is able to achieve good classification accuracy in a variety of domains, whatever the selection function or the similarity measure mentioned above. These new functions and measures highlight the importance of instance-based learning through lattice theory.