Soft Confidence-Weighted Learning

Online learning plays an important role in many big data mining problems because of its high efficiency and scalability. In the literature, many online learning algorithms using gradient information have been applied to solve online classification problems. Recently, more effective second-order algorithms have been proposed, where the correlation between the features is utilized to improve the learning efficiency. Among them, Confidence-Weighted (CW) learning algorithms are very effective, which assume that the classification model is drawn from a Gaussian distribution, which enables the model to be effectively updated with the second-order information of the data stream. Despite being studied actively, these CW algorithms cannot handle nonseparable datasets and noisy datasets very well. In this article, we propose a family of Soft Confidence-Weighted (SCW) learning algorithms for both binary classification and multiclass classification tasks, which is the first family of online classification algorithms that enjoys four salient properties simultaneously: (1) large margin training, (2) confidence weighting, (3) capability to handle nonseparable data, and (4) adaptive margin. Our experimental results show that the proposed SCW algorithms significantly outperform the original CW algorithm. When comparing with a variety of state-of-the-art algorithms (including AROW, NAROW, and NHERD), we found that SCW in general achieves better or at least comparable predictive performance, but enjoys considerably better efficiency advantage (i.e., using a smaller number of updates and lower time cost). To facilitate future research, we release all the datasets and source code to the public at http://libol.stevenhoi.org/.