Abstract
Three new classification methods for multi-tempotal data are proposed. They are named as a likelihood addition method, a majority method, and a Dempster's rule method. Basic strategies using these methods are to calculate likelihoods for each temporal data and to combine obtained likelihoods for final classification. These three methods use different combining algorithms. From classification experiments, following results were obtained. The method based on Dempster's rule of combination showed about 11% improvment of classification accuracies compared to a conventional method. This method needed about 16% more processing time than that of a conventional method. The other two proposed methods showed 1% to 5% increase of classification accuracies. However, processing times of these two methods were almost the same with that of a conventional method.
