Introduced a few years ago, analogy-based classification methods are a noticeable addition to the set of lazy learning techniques. They provide amazing results (in terms of accuracy) on many classical datasets. They look for all triples of examples in the training set that are in analogical proportion with the item to be classified on a maximal number of attributes and for which the corresponding analogical proportion equation on the class has a solution. In this paper when classifying a new item, we demonstrate a new approach where we focus on a small part of the triples available. To restrict the scope of the search, we first look for examples that are as similar as possible to the new item to be classified. We then only consider the pairs of examples presenting the same dissimilarity as between the new item and one of its closest neighbors. Thus we implicitly build triples that are in analogical proportion on all attributes with the new item. Then the classification is made on the basis of a majority vote on the pairs leading to a solvable class equation. This new algorithm provides results as good as other analogical classifiers with a lower average complexity.
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