A multi-scheme semi-supervised regression approach

doi:10.1016/j.patrec.2019.07.022

A multi-scheme semi-supervised regression approach

2019-07-01

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DOI：10.1016/j.patrec.2019.07.022

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The production of vast amounts of data has increased the necessity of applying Machine Learning (ML) and Pattern Recognition (PR) methods that could perform accurate predictive performance without demanding much human effort for collecting and preparing the necessary data. Keeping in mind that annotating instances is one of the most time-consuming procedures during the learning phase of supervised approaches, the role of Semi-supervised Learning (SSL) schemes, which exploit both labeled and unlabeled data, is totally upgraded considering especially the real-word scenarios. The flexibility that is offered through such schemes about combining various learners for mining useful information through unlabeled instances allows the production of several variants of these schemes. Thus, the construction of generic approaches that could achieve robust learning behavior over problems that stem from different scientific fields is the target of current research. Our contribution through this work is the proposal of a Multi-scheme Semi-supervised regression approach (MSSRA) that examines some well-defined conditions about the outputs of each contained learner and provides its decisions to a meta-level learner to produce the final predictions. The results over twenty-five well-known datasets prove the better generalization behavior of the proposed algorithm against the supervised version of the meta-level learner and two state-of-the-art semi-supervised regression (SSR) algorithms. (C) 2019 Elsevier B.V. All rights reserved.

Keywords:

Semi-supervised regression

Multi-scheme regression

Semi-supervised learning

Ensemble method

Machine learning

A multi-scheme semi-supervised regression approach

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