An Unsupervised Drift Detector for Online Imbalanced Evolving Streams

D. Himaja, T. Padmaja, P. Krishna

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.

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Paper Citation


in Harvard Style

Himaja D., Padmaja T. and Krishna P. (2019). An Unsupervised Drift Detector for Online Imbalanced Evolving Streams.In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-377-3, pages 225-232. DOI: 10.5220/0007926302250232


in Bibtex Style

@conference{data19,
author={D. Himaja and T. Padmaja and P. Krishna},
title={An Unsupervised Drift Detector for Online Imbalanced Evolving Streams},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2019},
pages={225-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007926302250232},
isbn={978-989-758-377-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - An Unsupervised Drift Detector for Online Imbalanced Evolving Streams
SN - 978-989-758-377-3
AU - Himaja D.
AU - Padmaja T.
AU - Krishna P.
PY - 2019
SP - 225
EP - 232
DO - 10.5220/0007926302250232