Multiple classifier systems for embedded string patterns

Multiple classifier systems area well proven and tested instrument for enhancing the recognition accuracy in statistical pattern recognition problems. However, there has been reported only little work on combining classifiers in structural pattern recognition. In this paper we describe a method for embedding strings into real vector spaces based on prototype selection, in order to gain several vectorial descriptions of the string data. We present methods for combining multiple classifiers trained on various vectorial data representations. As base classifiers we use nearest neighbor methods and support vector machine. In our experiments we demonstrate that this approach can be used to significantly improve the classification accuracy of string patterns.