Novel Class Detection in Data Streams

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)

Abstract

Data stream classification is challenging process as it involves consideration of many practical aspects associated with efficient processing and temporal of the stream. Two such aspects which are well studied and addressed by many present data stream classification techniques are infinite length and concept drift. Another very important characteristic of data streams, namely, concept-evolution is rarely being addressed in literature. Concept-evolution occurs as a result of new classes evolving in the stream. Handling concept evolution involves detecting novel classes and training the model with the same. It is a significant technique to mine the data where an important class is under-represented in the training set. This paper is an attempt to study and discuss the technique to handle this issue. We implement one of such state-of-art techniques and also modify for better performance.

Keywords

Novel class detection Data stream classification Concept evolution 

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Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.College of Engineering Pune Shivaji NagarPuneIndia

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