Information Fusion via a Hierarchical Neural Network Model
Yong Seog Kim · Journal of Computer Information Systems · 2016
This paper considers information fusion at the intermediate and raw data levels in order to improve the quality of decision making using currently available information. Information fusion becomes critical when records have been collected over certain periods of time at multiple locations with multiple systems. This is because they are often temporally and spatially correlated, and each system has its own bias and variance. To address these problems, a hierarchical neural network model is presented. The proposed model can learn both spatial and temporal dependence from data, and accommodate different information from multiple data sets. At the lower level in the proposed system, neural networks called local experts are specialized to learn spatial, temporal, or combined signals. At the upper level, another neural network called global expert is built. The global expert takes as inputs the estimates of local experts and searches the space of hypotheses to capture possible non-linear relationships among loc...