  The amount of data in our society has been exploding in the era of big data
today. In this paper, we address several open challenges of big data stream
classification, including high volume, high velocity, high dimensionality, high
sparsity, and high class-imbalance. Many existing studies in data mining
literature solve data stream classification tasks in a batch learning setting,
which suffers from poor efficiency and scalability when dealing with big data.
To overcome the limitations, this paper investigates an online learning
framework for big data stream classification tasks. Unlike some existing online
data stream classification techniques that are often based on first-order
online learning, we propose a framework of Sparse Online Classification (SOC)
for data stream classification, which includes some state-of-the-art
first-order sparse online learning algorithms as special cases and allows us to
derive a new effective second-order online learning algorithm for data stream
classification. In addition, we also propose a new cost-sensitive sparse online
learning algorithm by extending the framework with application to tackle online
anomaly detection tasks where class distribution of data could be very
imbalanced. We also analyze the theoretical bounds of the proposed method, and
finally conduct an extensive set of experiments, in which encouraging results
validate the efficacy of the proposed algorithms in comparison to a family of
state-of-the-art techniques on a variety of data stream classification tasks.
