Incremental weighted one-class classifier for mining stationary data streams

Big data analytics, especially data stream mining, is among the most popular contemporary machine learning problems. More and more often real-life tasks could generate massive and continuous amounts of data. Standard classifiers cannot cope with a large volume of the training set and/or changing nature of the environment. In this paper, we deal with a problem of continuously arriving objects, that with each time interval may contribute new, useful knowledge to the patter classification system. One-class classification is a very useful tool for stream analysis, as it can be used for tackling outliers, noise, appearance of new classes or imbalanced data to name a few. We propose a novel version of incremental One-Class Support Vector Machine, that assigns weights to each object according to its level of significance. This allows to train more robust one-class classifiers on incremental streams. We present two schemes for estimating weights for new, incoming data and examine their usefulness on a number of benchmark datasets. We also analyze time and memory requirements of our method. Results of experimental investigations prove, that our method can achieve better one-class recognition quality than algorithms used so far.