Active Learning with Spatial Distribution based Semi-Supervised Extreme Learning Machine for Multiclass Classification

Abstract

Unlabeled samples are often readily available in our daily lives. However, valuable information contained in a large number of unlabeled samples tends to be ignored by general supervised learning models. To make full use of unlabeled samples, we propose a novel framework that combines active learning with semi-supervised learning. On one hand, we expect to label as few samples as possible while achieving guaranteed classification performance, hence it's of vital importance to design a specific active learning strategy to select only the most valuable batch of samples for expert labeling. On the other hand, the introduction of distribution information in unlabeled sample pool will bring great benefits to the model. Both labeled samples and unlabeled samples can be used for training semi-supervised classification model. In this paper, uncertainty-based active learning and manifold-based semi-supervised learning are integrated into our framework. Extreme learning machine (ELM) is adopted as our base classifier. Moreover, a novel uncertainty criterion, called Bell-Function-based uncertainty, is proposed for active learning selection for the first time. Empirical results on six public benchmark datasets show that our algorithm produces better performance in comparison with other approaches.