Semiconormed possibility integrals and multi-feature pattern classification

The semiconormed possibility integrals are proposed as a multi-feature pattern classification model. A semiconormed possibility integral is a nonlinear integration of a function and its corresponding non-normalized possibility measures over feature space. The function of an object's feature vector represents the possibilities with uncertainty that the object belongs to a class. The uncertainty is due to the similar characteristics of objects from different classes and the distortion of the original characteristic information caused by feature data acquisition systems. The uncertainty is assessed by the non-normalized possibility measures, a possibility measure of a feature is considered as the credibility of the feature to provide reliable information for pattern classification. Integration of a function and the possibility measures effectively reduces the uncertainty and improves the pattern classification results. A pattern classification algorithm based on the semiconormed possibility integrals was used to classify a set of “ellipse data” and the well-known IRIS data, the classification results were compared with those obtained by using Bayes classifier.