Upper integral network with extreme learning mechanism

摘要

The upper integral is a type of non-linear integral with respect to non-additive measures, which represents the maximum potential of efficiency for a group of features with interaction. The value of upper integrals can be evaluated through solving a linear programming problem. Considering the upper integral as a classifier, this paper first investigates its implementation and performance. Fusing multiple upper integral classifiers together by using a single layer neural network, this paper considers a upper integral network as a classification system. The learning mechanism of ELM is used to train this single layer neural network. A comparison of performance between a single upper integral classifier and the upper integral network is given on a number of benchmark databases.