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Abstract: Even though, under representational restrictions, the nearest feature rules and the dissimilarity-based classifiers are feasible alternatives to the nearest neighbor method; individually, they may not be sufficiently powerful if a very small set of prototypes is required, e.g. when it is computationally expensive to deal with larger sets of prototypes. In this paper, we show that combining both strategies, taking advantage of their individual properties, provides an improvement, particularly for correlated data sets. The combined strategy consists in deriving an enriched (generalized) dissimilarity representation by using the nearest feature rules, namely feature lines and feature planes. On top of that enriched representation, Bayesian classifiers can be constructed in order to obtain a good generalization. [Copyright &y& Elsevier]

Abstract: A conventional way to discriminate between objects represented by dissimilarities is the nearest neighbor method. A more efficient and sometimes a more accurate solution is offered by other dissimilarity-based classifiers. They construct a decision rule based on the entire training set, but they need just a small set of prototypes, the so-called representation set, as a reference for classifying new objects. Such alternative approaches may be especially advantageous for non-Euclidean or even non-metric dissimilarities. The choice of a proper representation set for dissimilarity-based classifiers is not yet fully investigated. It appears that a random selection may work well. In this paper, a number of experiments has been conducted on various metric and non-metric dissimilarity representations and prototype selection methods. Several procedures, like traditional feature selection methods (here effectively searching for prototypes), mode seeking and linear programming are compared to the random selection. In general, we find out that systematic approaches lead to better results than the random selection, especially for a small number of prototypes. Although there is no single winner as it depends on data characteristics, the k-centres works well, in general. For two-class problems, an important observation is that our dissimilarity-based discrimination functions relying on significantly reduced prototype sets (3–10% of the training objects) offer a similar or much better classification accuracy than the best k-NN rule on the entire training set. This may be reached for multi-class data as well, however such problems are more difficult. [Copyright &y& Elsevier]

Usually, objects to be classified are represented by features. In this paper, we discuss an alternative object representation based on dissimilarity values. If such distances separate the classes well, the nearest neighbor method offers a good solution. However, dissimilarities used in practice are usually far from ideal and the performance of the nearest neighbor rule suffers from its sensitivity to noisy examples. We show that other, more global classification techniques are preferable to the nearest neighbor rule, in such cases. For classification purposes, two different ways of using generalized dissimilarity kernels are considered. In the first one, distances are isometrically embedded in a pseudo-Euclidean space and the classification task is performed there. In the second approach, classifiers are built directly on distance kernels. Both approaches are described theoretically and then compared using experiments with different dissimilarity measures and datasets including degraded data simulating the problem of missing values. [ABSTRACT FROM AUTHOR]