A drift detection method based on dynamic classifier selection

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Pinage, F (Author) Other; dos Santos, EM (Author) Other; João Gama (Author) FEP

Journal

Title: Data Mining and Knowledge Discovery

Vol. 34

Pages: 50-74

ISSN: 1384-5810

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 31 Citations

Scopus - 40 Citations

Other information

Authenticus ID: P-00R-97D

DOI: 10.1007/s10618-019-00656-w

Abstract (EN): Machine learning algorithms can be applied to several practical problems, such as spam, fraud and intrusion detection, and customer preferences, among others. In most of these problems, data come in streams, which mean that data distribution may change over time, leading to concept drift. The literature is abundant on providing supervised methods based on error monitoring for explicit drift detection. However, these methods may become infeasible in some real-world applications-where there is no fully labeled data available, and may depend on a significant decrease in accuracy to be able to detect drifts. There are also methods based on blind approaches, where the decision model is updated constantly. However, this may lead to unnecessary system updates. In order to overcome these drawbacks, we propose in this paper a semi-supervised drift detector that uses an ensemble of classifiers based on self-training online learning and dynamic classifier selection. For each unknown sample, a dynamic selection strategy is used to choose among the ensemble's component members, the classifier most likely to be the correct one for classifying it. The prediction assigned by the chosen classifier is used to compute an estimate of the error produced by the ensemble members. The proposed method monitors such a pseudo-error in order to detect drifts and to update the decision model only after drift detection. The achievement of this method is relevant in that it allows drift detection and reaction and is applicable in several practical problems. The experiments conducted indicate that the proposed method attains high performance and detection rates, while reducing the amount of labeled data used to detect drift.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 25