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Incremental Bayesian Classifier for Streaming Data with Concept Drift
Computer Engineering Department, Taiyuan Institute of Technology, Taiyuan, China.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
College of Software, Taiyuan University of Technology, Jinzhong, China.
Department of Information Engineering, Taiyuan City Vocational College, Taiyuan, China.
2023 (English)In: Lecture Notes on Data Engineering and Communications Technologies, Springer Science and Business Media Deutschland GmbH , 2023, Vol. 153, p. 509-518Chapter in book (Other academic)
Abstract [en]

Classification is an important task in the field of machine learning. Most classifiers based on offline learning are invalid for open data streams. In contrast, incremental learning is feasible for continuous data. This paper presents the Incremental Bayesian Classifier “Incremental_BC”, which continuously updates the probabilistic information according to each new training sample via recursive calculation. Further, the Incremental_BC is improved to deal with the flowing data whose distribution and property evolve with time, i.e., the concept drift. The effectiveness of the proposed methods has been verified by the results of simulation tests on benchmark data sets.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2023. Vol. 153, p. 509-518
Keywords [en]
Classification (of information), Data streams, E-learning, Bayesian classifier, Concept drifts, Continuous data, Data stream, Incremental learning, Machine-learning, Off-line learning, Online learning, Open datum, Streaming data, Open Data, Concept drift
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-61963DOI: 10.1007/978-3-031-20738-9_58ISI: 000964184200058Scopus ID: 2-s2.0-85147842399OAI: oai:DiVA.org:mdh-61963DiVA, id: diva2:1738690
Available from: 2023-02-22 Created: 2023-02-22 Last updated: 2023-05-03Bibliographically approved

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Wu, PengXiong, Ning

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