Learning by Analogy: A Classification Rule for Binary and Nominal Data

Abstract

This paper deals with learning to classify by using an approximation of the analogical proportion between four objects. These objects are described by binary and nominal attributes. Firstly, the paper recalls what is an analogical proportion between four objects, then it introduces a measure called analogical dissimilarity, reflecting how close four objects are from being in an analogical proportion. Secondly, it presents an analogical instance-based learning method and describes a fast algorithm. Thirdly, a technique to assign a set of weights to the attributes of the objects is given: a weight is chosen according to the type of the analogical proportion involved. The weights are obtained from the learning sample. Then, some results of the method are presented. They compare favorably to standard classification techniques on six benchmarks. Finally, the relevance and complexity of the method are discussed.