Multiscale Drift Detection Test to Enable Fast Learning in Nonstationary Environments

Document Type

Article

Publication Date

7-1-2021

Abstract

A model can be easily influenced by unseen factors in nonstationary environments and fail to fit dynamic data distribution. In a classification scenario, this is known as a concept drift. For instance, the shopping preference of customers may change after they move from one city to another. Therefore, a shopping website or application should alter recommendations based on its poorer predictions of such user patterns. In this article, we propose a novel approach called the multiscale drift detection test (MDDT) that efficiently localizes abrupt drift points when feature values fluctuate, meaning that the current model needs immediate adaption. MDDT is based on a resampling scheme and a paired student t-test. It applies a detection procedure on two different scales. Initially, the detection is performed on a broad scale to check if recently gathered drift indicators remain stationary. If a drift is claimed, a narrow scale detection is performed to trace the refined change time. This multiscale structure reduces the massive time of constantly checking and filters noises in drift indicators. Experiments are performed to compare the proposed method with several algorithms via synthetic and real-world datasets. The results indicate that it outperforms others when abrupt shift datasets are handled, and achieves the highest recall score in localizing drift points.

Identifier

85109201594 (Scopus)

Publication Title

IEEE Transactions on Cybernetics

External Full Text Location

https://doi.org/10.1109/TCYB.2020.2989213

e-ISSN

21682275

ISSN

21682267

PubMed ID

32544055

First Page

3483

Last Page

3495

Issue

7

Volume

51

Grant

51775385

Fund Ref

National Natural Science Foundation of China

This document is currently not available here.

Share

COinS