CAAL

Traditional active learning encounters a cold start issue when very few labelled examples are present for learninga decent initial classifier. Its poor quality subsequently affects selection of the next query and stability of the iterative learning process, resulting in more annotation effort from a domain expert. To address this issue, this paper presents a novel class augmentation technique, which enhances each class’s representation which initially consists of limited labelled example set. Our augmentation employs a connectivity-based influence computation algorithm with an incorporated decaying mechanism for the unlabelled samples. Besides augmentation, our method also introduces structure preserving oversampling to correct class imbalance. Extensive experiments on ten publicly available data sets demonstrate the effectiveness of our proposed method over existing state-of-the-art methods. Moreover, our proposed modules perform at the fundamental data level without any requirement to modify the well-established standard machine learning tools.