MODS: Multiple One-class Data Streams Learning from Homogeneous Data

This paper presents a novel approach, called MODS, to build an accurate time evolving classifier from multiple one-class data streams learning time evolving classifier. Our proposed MODS approach works in two steps. In the first step, we first construct local one-class classifiers on the labeled positive examples from each sub-data stream respectively. We then collect the informative examples (support vectors) around each local one-class classifier, which can support the decision boundary of the classifier. This is called support vector preservation principle. In the second step, we construct a global one-class classifier on the collected informative examples. By using the support vector preservation principle, our proposed MODS explicitly addresses the problem of building accurate classifier from multiple one-class data streams. Extensive experiments on real life data streams have demonstrated that our MODS approach can achieve high performance and efficiency for the multiple one-class data streams learning in comparison with other approaches.