One of the key barriers to generalizing correct and useful rules from lengthy and jumbled data is that there is a large amount of information in the object which is irrelevant to the result or has little relevance to the result. In this paper, the grey relational degree is improved to make it suitable for 0–1 data sets, so the test data can be preprocessed. The rough set theory and association rules are integrated, the accuracy of rule induction is improved. With the help of the fission and breed characteristic of genetic algorithm, the basic test database is extended, so the problem of the lack of test data cases is solved. Finally, the paper applies the example to verify the algorithm.


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    Titel :

    Multi-Factor Induction Based on Grey Relational Degree and Rough Set


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Yan, Liang (Herausgeber:in) / Duan, Haibin (Herausgeber:in) / Yu, Xiang (Herausgeber:in) / Wang, Taoyu (Autor:in) / Shi, Xianjun (Autor:in) / Chen, Qijie (Autor:in)


    Erscheinungsdatum :

    2021-10-30


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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