Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams

Authors:
Mohammad M. Masud;Jing Gao;Latifur Khan;Jiawei Han;Bhavani Thuraisingham
Affiliations:
University of Texas, Dallas;University of Illinois, Urbana Champaign;University of Texas, Dallas;University of Illinois, Urbana Champaign;University of Texas, Dallas
Venue:
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Year:
2009

Citing 9
Cited 6

Mining time-changing data streams

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Topic-conditioned novelty detection

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining concept-drifting data streams using ensemble classifiers

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Novelty detection: a review—part 1: statistical approaches

Signal Processing
Combining proactive and reactive predictions for data streams

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Using additive expert ensembles to cope with concept drift

ICML '05 Proceedings of the 22nd international conference on Machine learning
OLINDDA: a cluster-based approach for detecting novelty and concept drift in data streams

Proceedings of the 2007 ACM symposium on Applied computing
A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data

ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Stop Chasing Trends: Discovering High Order Models in Evolving Data

ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering

Classification and novel class detection of data streams in a dynamic feature space

ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Improving gaussian process classification with outlier detection: with applications in image classification

ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Classification and novel class detection in data streams with active mining

PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
A study of decision tree induction for data stream mining using boosting genetic programming classifier

SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Temporal contexts: Effective text classification in evolving document collections

Information Systems
Novel class detection within classification for data streams

ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

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.