Abstract

Recent findings in pattern recognition show that dramatic improvement of the recognition rate can be obtained by application of fusion systems utilizing many different and diverse classifiers for the same task. Apart from a good individual performance of individual classifiers the most important factor is the useful diversity they exhibit. In this work we present an example of a novel non-parametric classifier design, which shows a substantial level of diversity with respect to other commonly used classifiers. In our approach inspiration for the new classification method has been found in the physical world. Namely we considered training data as particles in the input space and exploited the concept of a static field acting upon the samples. Specifically, every single data point used for training was a source of a central field, curving the geometry of the input space. The classification process is presented as a translocation in the input space along the local gradient of the field potential generated by the training data. The label of a training sample to which it converged during the translocation determines the eventual class label of the new data point. Based on selected simple fields found in nature, we show extensive examples and visual interpretations of the presented classification method. The practical applicability of the new model is examined and tested using well-known real and artificial datasets.