Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams
Abstract
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 that can automatically detect the emergence of a novel class in the presence of concept-drift by quantifying cohesion among unlabeled test instances, and separation of the test instances from training instances. Our approach is non-parametric, meaning, it does not assume any underlying distributions of data. Comparison with the state-of-the-art stream classification techniques prove the superiority of our approach.
Cite
Text
Masud et al. "Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04174-7_6Markdown
[Masud et al. "Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/masud2009ecmlpkdd-integrating/) doi:10.1007/978-3-642-04174-7_6BibTeX
@inproceedings{masud2009ecmlpkdd-integrating,
title = {{Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams}},
author = {Masud, Mohammad M. and Gao, Jing and Khan, Latifur and Han, Jiawei and Thuraisingham, Bhavani},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2009},
pages = {79-94},
doi = {10.1007/978-3-642-04174-7_6},
url = {https://mlanthology.org/ecmlpkdd/2009/masud2009ecmlpkdd-integrating/}
}