A cluster-assumption based batch mode active learning technique

In this paper, we propose an active learning technique for solving multiclass problems with support vector machine (SVM) classifiers. The technique is based on both uncertainty and diversity criteria. The uncertainty criterion is implemented by analyzing the one-dimensional output space of the SVM classifier. A simple histogram thresholding algorithm is used to find out the low density region in the SVM output space to identify the most uncertain samples. Then the diversity criterion exploits the kernel k-means clustering algorithm to select uncorrelated informative samples among the selected uncertain samples. To assess the effectiveness of the proposed method we compared it with other batch mode active learning techniques presented in the literature using one toy data set and three real data sets. Experimental results confirmed that the proposed technique provided a very good tradeoff among robustness to biased initial training samples, classification accuracy, computational complexity, and number of new labeled samples necessary to reach the convergence.

► A novel SVM-based active learning classification technique is presented.
► Uncertainty of samples is assessed by a criterion based on the cluster assumption.
► Diversity of uncertain samples is assessed by using the kernel k-means clustering.
► The method is robust to the presence of strongly biased initial training samples.
► The technique exhibits high accuracy and is computational fast and robust.