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  • 1.
    Wu, Peng
    et al.
    Computer Engineering Department, Taiyuan Institute of Technology, Taiyuan, China.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Li, Gang
    College of Software, Taiyuan University of Technology, Jinzhong, China.
    lv, Jinrui
    Department of Information Engineering, Taiyuan City Vocational College, Taiyuan, China.
    Incremental Bayesian Classifier for Streaming Data with Concept Drift2023In: 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.

  • 2.
    Wu, Peng
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China..
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Malardalen Univ, Sch Innovat Design & Engn, S-72123 Vasteras, Sweden..
    Xiong, Juxia
    Guangxi Univ Nationalities, Sch Math & Phys, Nanning 530006, Peoples R China..
    Wu, Jinzhao
    Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China.;Guangxi Univ Nationalities, Sch Math & Phys, Nanning 530006, Peoples R China..
    Reasoning Method between Polynomial Error Assertions2021In: Information, E-ISSN 2078-2489, Vol. 12, no 8, article id 309Article in journal (Refereed)
    Abstract [en]

    Error coefficients are ubiquitous in systems. In particular, errors in reasoning verification must be considered regarding safety-critical systems. We present a reasoning method that can be applied to systems described by the polynomial error assertion (PEA). The implication relationship between PEAs can be converted to an inclusion relationship between zero sets of PEAs; the PEAs are then transformed into first-order polynomial logic. Combined with the quantifier elimination method, based on cylindrical algebraic decomposition, the judgment of the inclusion relationship between zero sets of PEAs is transformed into judgment error parameters and specific error coefficient constraints, which can be obtained by the quantifier elimination method. The proposed reasoning method is validated by proving the related theorems. An example of intercepting target objects is provided, and the correctness of our method is tested through large-scale random cases. Compared with reasoning methods without error semantics, our reasoning method has the advantage of being able to deal with error parameters.

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