Journal of Information Processing Systems

  • 제20권2호
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  • Pages.215-225
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  • 2024
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Construction of an Internet of Things Industry Chain Classification Model Based on IRFA and Text Analysis

  • Zhimin Wang (School of Economics and Management, Zhongyuan University of Technology)
  • 투고 : 2023.02.14
  • 심사 : 2023.03.21
  • 발행 : 2024.04.30
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초록

With the rapid development of Internet of Things (IoT) and big data technology, a large amount of data will be generated during the operation of related industries. How to classify the generated data accurately has become the core of research on data mining and processing in IoT industry chain. This study constructs a classification model of IoT industry chain based on improved random forest algorithm and text analysis, aiming to achieve efficient and accurate classification of IoT industry chain big data by improving traditional algorithms. The accuracy, precision, recall, and AUC value size of the traditional Random Forest algorithm and the algorithm used in the paper are compared on different datasets. The experimental results show that the algorithm model used in this paper has better performance on different datasets, and the accuracy and recall performance on four datasets are better than the traditional algorithm, and the accuracy performance on two datasets, P-I Diabetes and Loan Default, is better than the random forest model, and its final data classification results are better. Through the construction of this model, we can accurately classify the massive data generated in the IoT industry chain, thus providing more research value for the data mining and processing technology of the IoT industry chain.

키워드

과제정보

This research was supported by 2021 Key Research Projects of Henan Higher Education Institutions: Ideas and Countermeasures for Building IoT Industry Cluster in Henan under the Background of 5G (No. 21A790024).

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