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Advanced Hybrid Information Processing. 7th EAI International Conference, ADHIP 2023, Harbin, China, September 22-24, 2023, Proceedings, Part I

Research Article

An Online Integrated Classification Algorithm for Innovation and Entrepreneurship Teaching Data Based on Decision Tree

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-50543-0_25,
        author={Juanjuan Zou},
        title={An Online Integrated Classification Algorithm for Innovation and Entrepreneurship Teaching Data Based on Decision Tree},
        proceedings={Advanced Hybrid Information Processing. 7th EAI International Conference, ADHIP 2023, Harbin, China, September 22-24, 2023, Proceedings, Part I},
        proceedings_a={ADHIP},
        year={2024},
        month={3},
        keywords={Decision Tree Innovation and Entrepreneurship Data Classification Differential Grey Wolf Optimization Pruning Decision Tree},
        doi={10.1007/978-3-031-50543-0_25}
    }
    
  • Juanjuan Zou
    Year: 2024
    An Online Integrated Classification Algorithm for Innovation and Entrepreneurship Teaching Data Based on Decision Tree
    ADHIP
    Springer
    DOI: 10.1007/978-3-031-50543-0_25
Juanjuan Zou1,*
  • 1: Chongqing Vocational Institute of Engineering
*Contact email: 17726637816@163.com

Abstract

In order to improve the accuracy of data classification, reduce misclassification rates, and improve classification efficiency, a decision tree based online integrated classification algorithm for innovation and entrepreneurship teaching data is proposed. Establish a prototype system for online integration of innovation and entrepreneurship teaching data, and based on the results of data integration, preliminarily construct a decision tree model. Then, the fuzzy decision tree obtained by combining fuzzy theory with decision tree is used to solve the problems of data imbalance and missing data types. In order to further reduce the misclassification rate of data, the differential grey wolf optimization algorithm is used to optimize the decision tree, and the decision tree is improved through operations such as feature selection and decision tree pruning to obtain the optimal classification results of innovation and entrepreneurship teaching data. The experimental results show that the proposed method has high data classification accuracy, low misclassification rate, and high classification efficiency, which verifies the effectiveness of the method.

Keywords
Decision Tree Innovation and Entrepreneurship Data Classification Differential Grey Wolf Optimization Pruning Decision Tree
Published
2024-03-24
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-50543-0_25
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