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An Efficient Metric for Heterogeneous Inductive Learning Applications in the Attribute-Value Language

Christophe Giraud-Carrier, Tony Martinez, An Efficient Metric for Heterogeneous Inductive Learning Applications in the Attribute-Value Language. Intelligent Systems (Proceedings of GWIC'94), pp. 341–350. January 1995. PDF, 49 Kbytes.

Abstract

Many inductive learning problems can be expressed in the classical attribute-value language. In order to learn and to generalize, learning systems often rely on some measure of similarity between their current knowledge base and new information. The attribute-value language defines a heterogeneous multi-dimensional input space, where some attributes are nominal and others linear. Defining similarity, or proximity, of two points in such input spaces is non trivial. We discuss two representative homogeneous metrics and show examples of why they are limited to their own domains. We then address the issues raised by the design of a heterogeneous metric for inductive learning systems. In particular, we discuss the need for normalization and the impact of don't-care values. We propose a heterogeneous metric and evaluate it empirically on a simplified version of ILA.

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