One-Class-Based Uncertain Data Stream Learning

This paper presents a novel approach to one-class-based uncertain data stream learning. Our proposed approach works in three steps. Firstly, we put forward a local kerneldensity-based method to generate a bound score for each instance, which refines the location of the corresponding instance. Secondly, we construct an uncertain one-class classifier by incorporating the generated bound score into a one-class SVM-based learning phase. Thirdly, we devise an ensemble classifier, integrated from uncertain one-class classifiers built on the current and historical chunks, to cope with the concept drift involved in the uncertain data stream environment. Our proposed method explicitly handles the uncertainty of the input data and enhances the ability of oneclass learning in reducing the sensitivity to noise. Extensive experiments on uncertain data streams demonstrate that our proposed approach can achieve better performance and is highly robust to noise in comparison with state-of-the-art one-class learning method.