A clustering based variable sub-window approach using particle swarm optimisation for biomedical sensor data monitoring. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Lan, Kun; Fong, Simon; Liu, Lian-Sheng; Wong, Raymond K.; Dey, Nilanjan; Millham, Richard C.; Wong, Kelvin K.L.
• Is Part Of: Enterprise information systems. Volume 15:Issue 1(2021)
• Page Start: 15
• Page End: 35
• Publication Date: 2021-01-02
• Subjects: Data stream mining -- biomedical healthcare -- sensor -- pre-processing -- metaheuristics -- adaptive windowing; Information storage and retrieval systems -- Periodicals; Management information systems -- Periodicals; Electronic commerce -- Periodicals; 658.4038011
• DOI: 10.1080/17517575.2019.1597388
• Languages: English
• ISSNs: 1751-7575

Abstract

ABSTRACT: Advances in information technologies enable data to be ubiquitously generated from sensors, especially in the industrial healthcare research and application fields. The aim is to develop an adaptive windowing pre-processing approach using clustering-based metaheuristics search for biomedical data stream classification, which uses a sliding window to scan the multivariate data stream segment to segment. Our new model is put under test with other temporal data stream pre-processing methods on those biomedical sensor datasets. The experiments give higher accuracy and less time cost especially in dynamically adjusting the window size according to clustering outcomes that are optimised by metaheuristics.