Incremental Bayesian Classifier for Streaming Data with Concept Drift

被引：0

作者：

Wu, Peng ^{[1
,2
]}

Xiong, Ning ^{[2
]}

Li, Gang ^{[3
]}

Lv, Jinrui ^{[4
]}

机构：

[1] Taiyuan Inst Technol, Comp Engn Dept, Taiyuan 030008, Peoples R China

[2] Malardalen Univ, Sch Innovat Design & Engn, S-72123 Vasteras, Sweden

[3] Taiyuan Univ Technol, Coll Software, Jinzhong 030600, Peoples R China

[4] Taiyuan City Vocat Coll, Dept Informat Engn, Taiyuan 030027, Peoples R China

来源：

ADVANCES IN NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, ICNC-FSKD 2022 | 2023年 / 153卷

关键词：

Incremental learning; Online learning; Bayesian classifier; Concept drift; SYSTEM;

D O I：

10.1007/978-3-031-20738-9_58

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：509 / 518

页数：10

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