Xuefeng Xi1, Victor S. Sheng1,2,*, Binqi Sun2, Lei Wang1, Fuyuan Hu1
CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 185-198, 2018, DOI:10.3970/cmc.2018.03694
Abstract Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables. It has received relatively small attention from the Machine Learning community. However, multi-target regression exists in many real-world applications. In this paper we conduct extensive experiments to investigate the performance of three representative multi-target regression learning algorithms (i.e. Multi-Target Stacking (MTS), Random Linear Target Combination (RLTC), and Multi-Objective Random Forest (MORF)), comparing the baseline single-target learning. Our experimental results show that all three multi-target regression learning algorithms do improve the performance of the single-target More >
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