A multiple combining method for optimizing dissimilarity-based classification

Abstract

This paper reports an experimental study on a multiple combining method for optimizing dissimilarity-based classifications (DBCs) by simultaneously using a dynamic time warping (DTW) and a multiple fusion strategy (MFS). DBCs are a way of defining classifiers among classes; they are not based on the feature measurements of individual samples, but rather on a suitable dissimilarity measure among the samples. In DTW, the dissimilarity is measured in two steps: first, we adjust the object samples by finding the best warping path with a correlation coefficient-based DTW technique. We then compute the dissimilarity distance between the adjusted objects with conventional measures. In MFS, fusion strategies are repeatedly used in generating dissimilarity matrices as well as in designing classifiers: we first combine the dissimilarity matrices obtained with the DTW technique to a new matrix. After training some base classifiers in the new matrix, we again combine the results of the base classifiers. Our experimental results for well-known benchmark databases demonstrate that the proposed mechanism works well and achieves further improved results in terms of the classification accuracy compared with the previous approaches. © 2010 Springer-Verlag Berlin Heidelberg.