Big Data Analytics with Applications in Insider Threat Detection

11

Classification and Novel Class Detection in Concept-Drifting Data Streams

11.1 Introduction

As discussed in [MASU09], in a typical data stream classification task, it is assumed that the total number of classes are fixed. This assumption may not be valid in a real streaming environment, where new classes may evolve. 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. The problem becomes more challenging in the presence of concept drift, when the underlying data distribution changes over time. We propose a novel and efficient technique ...