ABSTRACT
This chapter discusses proposed framework for classifying data streams with automatic novel class detection mechanism. It describes the ECS Miner algorithm and Classification with novel class detection. Traditional data stream classification techniques are not capable of recognizing novel class instances until the appearance of the novel class is manually identified, and labeled instances of that class are presented to the learning algorithm for training. A comparison with the state-of-the-art stream classification techniques proves the superiority of authors’ approach. The algorithm consists of two main parts: classification and novel class detection Algorithm sketches the classification and novel class detection technique. SynC simulates only concept drift, with no novel classes. SynC data are generated with a moving hyperplane. However, OLI N DDA assumes that there is only one "normal" class, and all other classes are "novel." Most of the novelty detection techniques either assume that there is no concept drift or build a model for a single "normal" class.
