Authors:
D. Himaja
1
;
T. Maruthi Padmaja
1
and
P. Radha Krishna
2
Affiliations:
1
Department of Computer Science and Engineering, Vignan’s Foundation for Science, Technology and Research (Deemed to be University), Guntur-Tenali Rd, Vadlamudi, Guntur, Andhra Pradesh and India
;
2
Department of Computer Science and Engineering, National Institute of Technology, Warangal, Telangana and India
Keyword(s):
Class Imbalance, Evolving Stream, Concept Drift, Active Learning, Hypothesis Tests.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Business Analytics
;
Computational Intelligence
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Predictive Modeling
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Support Vector Machines and Applications
;
Theory and Methods
Abstract:
Detecting concept drift from an imbalanced evolving stream is challenging task. At high degree of imbalance ratios, the poor or nil performance estimates of the learner from minority class tends to drift detection failures. To ameliorate this problem, we propose a new drift detection and adaption framework. Proposed drift detection mechanism is carried out in two phases includes unsupervised and supervised drift detection with queried labels. The adaption framework is based on the batch wise active learning. Comparative results on four synthetic and one real world balanced and imbalanced evolving streams with other prominent drift detection methods indicates that our approach is better in detecting the drift with low false positive rates.