Pattern classification method by integrating interval feature values

Pattern classification based on Bayesian statistical decision theory needs a complete knowledge of the probability laws to perform the classification. In the actual pattern classification, however, it is generally impossible to get the complete knowledge as constant feature values because of the influence of noise. A pattern classification theory using feature values defined on a closed interval is formalized in the framework of the Dempster-Shafer measure. Then, in order to make up missing information, a new integration algorithm is proposed