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Multi-target regression is concerned with the prediction of multiplecontinuous target variables using a shared set of predictors. Two keychallenges in multi-target regression are: (a) modelling target dependenciesand (b) scalability to large output spaces. In this paper, a new multi-targetregression method is proposed that tries to jointly address these challengesvia a novel problem transformation approach. The proposed method, called MRQ,is based on the idea of quantizing the output space in order to transform themultiple continuous targets into one or more discrete ones. Learning on thetransformed output space naturally enables modeling of target dependencieswhile the quantization strategy can be flexibly parameterized to control thetrade-off between prediction accuracy and computational efficiency. Experimentson a large collection of benchmark datasets show that MRQ is both highlyscalable and also competitive with the state-of-the-art in terms of accuracy.In particular, an ensemble version of MRQ obtains the best overall accuracy,while being an order of magnitude faster than the runner up method.

Multi-target regression via output space quantization