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A crucial issue in dissimilarity-based classification is the choice of the representation set. In the small sample case, classifiers capable of a good generalization and the injection or addition of extra information allow to overcome the representational limitations. In this paper, we present a new approach for enriching dissimilarity representations. It is based on the concept of feature lines and consists in deriving a generalized version of the original dissimilarity representation by using feature lines as prototypes. We use a linear normal density-based classifier and the nearest neighbor rule, as well as two different methods for selecting prototypes: random choice and a length-based selection of the feature lines. An important observation is that just a few long feature lines are needed to obtain a significant improvement in performance over the other representation sets and classifiers. In general, the experiments show that this alternative representation is especially profitable for some correlated datasets.

Abstract. The analysis and classification of seismic patterns, which are typically registered as digital signals, can be used to monitor and understand the underlying geophysical phenomena beneath the volcanoes. In recent years, there has been an increasing interest in the development of automated systems for labeling those signals according to a number of pre-defined volcanic, tectonic and environmental classes. The first and crucial stage in the design of such systems is the definition or adoption of an appropriate representation of the raw seismic signals, in such a way that the subsequent stage -classification-is made easier or more accurate. This paper describes and discusses the most common representations that have been applied in the literature on classification of seismic-volcanic signals; namely, time-frequency features and cepstral coefficients. A comparative study of them is performed in terms of two criteria: (i) the leave-one-out nearest neighbor error, which provides a parameterless measure of the discriminative representational power and (ii) a visual examination of the representational quality via a scatter plot of the best three selected features.

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