A multiple criteria active learning method for support vector regression

This paper presents a novel active learning method developed in the framework of ε-insensitive support vector regression (SVR) for the solution of regression problems with small size initial training data. The proposed active learning method selects iteratively the most informative as well as representative unlabeled samples to be included in the training set by jointly evaluating three criteria: (i) relevancy, (ii) diversity, and (iii) density of samples. All three criteria are implemented according to the SVR properties and are applied in two clustering-based consecutive steps. In the first step, a novel measure to select the most relevant samples that have high probability to be located either outside or on the boundary of the ε-tube of SVR is defined. To this end, initially a clustering method is applied to all unlabeled samples together with the training samples that are inside the ε-tube (those that are not support vectors, i.e., non-SVs); then the clusters with non-SVs are elimi...