Extensive semi-quantitative regression

In this paper, we propose and solve a new machine learning problem called the extensive semi-quantitative regression, where the information about some target values is incomplete; we only know their lower bounds and/or upper bounds instead of their exact values. To employ the information efficiently in extensive semi-quantitative regression, we introduce a local graph to capture the geometric structure for the samples with the exact target values and the target bounds, and construct a graph-based support vector regressor, called ESQ-SVR. The efficiency of our ESQ-SVR is supported by the results of preliminary experiments conducted on both the artificial and real world datasets. HighlightsWe propose a new problem called extensive semi-quantitative regression.A graph-based support vector regressor is used to solve the above problem.Our approach could capture the geometric structure for the samples.It also bounded the target values and the value ranges.The efficiency of the approach is supported by the results of experiments.

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