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In classifier (or regression) fusion the aim is to combine the outputs ofseveral algorithms to boost overall performance. Standard supervised fusionalgorithms often require accurate and precise training labels. However,accurate labels may be difficult to obtain in many remote sensing applications.This paper proposes novel classification and regression fusion models that canbe trained given ambiguosly and imprecisely labeled training data in whichtraining labels are associated with sets of data points (i.e., "bags") insteadof individual data points (i.e., "instances") following a multiple instancelearning framework. Experiments were conducted based on the proposed algorithmson both synthetic data and applications such as target detection and crop yieldprediction given remote sensing data. The proposed algorithms show effectiveclassification and regression performance.

Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications