@article { , title = {Burst detection-based selective classifier resetting.}, abstract = {Concept drift detection algorithms have historically been faithful to the aged architecture of forcefully resetting the base classifiers for each detected drift. This approach prevents underlying classifiers becoming outdated as the distribution of a data stream shifts from one concept to another. In situations where both concept drift and temporal dependence are present within a data stream, forced resetting can cause complications in classifier evaluation. Resetting the base classifier too frequently when temporal dependence is present can cause classifier performance to appear successful, when in fact this is misleading. In this research, a novel architectural method for determining base classifier resets, Burst Detection-based Selective Classifier Resetting (BD-SCR), is presented. BD-SCR statistically monitors changes in the temporal dependence of a data stream to determine if a base classifier should be reset for detected drifts. The experimental process compares the predictive performance of state-of-the-art drift detectors in comparison to the "No-Change" detector using BD-SCR to inform and control the resetting decision. Results show that BD-SCR effectively reduces the negative impact of temporal dependence during concept drift detection through a clear negation in the performance of the "No-Change" detector, but is capable of maintaining the predictive performance of state-of-the-art drift detection methods.}, doi = {10.1142/s0219649221500271}, eissn = {1793-6926}, issn = {0219-6492}, issue = {2}, journal = {Journal of information and knowledge management}, note = {INFO COMPLETE (Now published, checked and updated 18/5/2021 LM; notified by contact 06.05.2021 GB) PERMISSION GRANTED (version = AAM ; embargo = 12 months ; licence = publisher's own ; https://www.worldscientific.com/page/authors/author-rights ; 06.05.2021 GB) DOCUMENT READY (rec'd from contact 06.05.2021 GB) ADDITIONAL INFO: Scott Wares; John Isaacs; Eyad Elyan Electronic version of an article published as WARES, S., ISAACS, J. and ELYAN, E. 2021. Burst detection-based selective classifier resetting. Journal of information and knowledge management [online], 20(2), article 2150027. Available from: https://doi.org/10.1142/S0219649221500271 © copyright World Scientific Publishing Company. Journal homepage: https://www.worldscientific.com/worldscinet/jikm}, publicationstatus = {Published}, publisher = {World Scientific Publishing}, url = {https://rgu-repository.worktribe.com/output/1328791}, volume = {20}, keyword = {Interactive Machine Vision, Data streaming, Concept drift, Temporal dependence}, year = {2021}, author = {Wares, Scott and Isaacs, John and Elyan, Eyad} }