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Multi-Output Dependence (MOD) learning is a generalization of standardclassification problems that allows for multiple outputs that are dependent oneach other. A primary issue that arises in the context of MOD learning is thatfor any given input pattern there can be multiple correct output patterns. Thischanges the learning task from function approximation to relationapproximation. Previous algorithms do not consider this problem, and thuscannot be readily applied to MOD problems. To perform MOD learning, weintroduce the Hierarchical Multi-Output Nearest Neighbor model (HMONN) thatemploys a basic learning model for each output and a modified nearest neighborapproach to refine the initial results.

A Hierarchical Multi-Output Nearest Neighbor Model for Multi-Output Dependence Learning