Construction of an Internet of Things Industry Chain Classification Model Based on IRFA and Text Analysis
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초록
With the rapid development of Internet of Things (IoT) and big data technology, a large amount of data will begenerated during the operation of related industries. How to classify the generated data accurately has becomethe core of research on data mining and processing in IoT industry chain. This study constructs a classificationmodel of IoT industry chain based on improved random forest algorithm and text analysis, aiming to achieveefficient and accurate classification of IoT industry chain big data by improving traditional algorithms. Theaccuracy, precision, recall, and AUC value size of the traditional Random Forest algorithm and the algorithmused in the paper are compared on different datasets. The experimental results show that the algorithm modelused in this paper has better performance on different datasets, and the accuracy and recall performance on fourdatasets are better than the traditional algorithm, and the accuracy performance on two datasets, P-I Diabetesand 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 IoTindustry chain, thus providing more research value for the data mining and processing technology of the IoTindustry chain.
Abstract
With the rapid development of Internet of Things (IoT) and big data technology, a large amount of data will begenerated during the operation of related industries. How to classify the generated data accurately has becomethe core of research on data mining and processing in IoT industry chain. This study constructs a classificationmodel of IoT industry chain based on improved random forest algorithm and text analysis, aiming to achieveefficient and accurate classification of IoT industry chain big data by improving traditional algorithms. Theaccuracy, precision, recall, and AUC value size of the traditional Random Forest algorithm and the algorithmused in the paper are compared on different datasets. The experimental results show that the algorithm modelused in this paper has better performance on different datasets, and the accuracy and recall performance on fourdatasets are better than the traditional algorithm, and the accuracy performance on two datasets, P-I Diabetesand 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 IoTindustry chain, thus providing more research value for the data mining and processing technology of the IoTindustry chain.
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