Fuzzy Multioutput Transfer Learning for Regression

Abstract

Multioutput regression aims to predict multiple continuous outputs simultaneously using the common set of input variables. The significant challenge arises from modeling relevance between inputs and outputs. Moreover, the shortage of labeled multioutput data and the divergence of data are other factors that impede the development of multioutput regression problems. The recent emergence of transfer learning techniques, which have the ability of leveraging previously acquired knowledge from a similar domain, provide a solution to the above issues. In this article, a novel fuzzy transfer learning method is proposed to tackle the multioutput regression problems in homogeneous and heterogeneous scenarios. By considering output–input dependencies and inter-output correlations, fuzzy rules are extracted to reflect the shared characteristics of different outputs and capture their uniqueness. For a homogeneous scenario, fuzzy rules are first accumulated in a related domain (called the source domain), which has a sufficient amount of training data. Based on different transform strategies, the fuzzy rules are then transferred to improve the new but similar regression tasks in the current domain (called the target domain), where only a few data have multiple responses. On this basis, we handle a more complex heterogeneous scenario by learning a latent input space to reduce the disagreement of variables between domains. The experiment results on thirteen real-world datasets with multiple outputs illustrate the effectiveness of our method. The impact of core coefficients on performance is also analyzed.