Prediction of Tariff Package Model Using ROF-LGB Algorithm

Zheng, NaiSong; Jiang, Xinyu; Ao, Yibo; Zhao, Xibin

doi:10.1145/3352411.3352421

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…where h m is the newly added tree to minimize the loss L, and γ m is the step length. GBRT requires a decision tree of fixed size for weak learning [20,21]. The AdaBoost regressor is a meta-estimator that begins by fitting a regression to the original dataset and then fitting additional copies of the regression to the same dataset, but the weight of the instances is adjusted according to the error of the current prediction [22].…”

Section: Regression Algorithmsmentioning

confidence: 99%

Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms

Jin

et al. 2020

Applied Sciences

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The application of visible near-infrared (VIS-NIR) analysis technology to quantify the nutrients in soil has been widely recognized. It is important to improve the performance of regression models that can predict the soil-available potassium concentration. This study collected soil samples from southern Anhui, China, and concentrated on the modelling methods by using 29 pretreatment methods. The results show that a combination of three methods, Savitzky–Golay, standard normal variate, and dislodge tendency, exhibited better stability than others because it was the most capable of achieving levels A and B of the ratio of performance of deviation. The boosting algorithms that form an ensemble of multiple weak predictors exhibited better performance than partial least square (PLS) regression and support vector regression (SVR) for the prediction of soil-available potassium. These regression models could be employed to precisely predict the soil-available potassium concentration.

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Section: Regression Algorithmsmentioning

confidence: 99%

Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms

Jin

et al. 2020

Applied Sciences

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“…By analyzing objective statistical data, we can discover the inner connection and patterns between different indicators from them and build potential inference and prediction models as a way to perform specific tasks such as classification and prediction 3 . In this paper, we use data mining technology to analyze 5G user characteristics and design models to make predictions on whether users are potential 5G users, providing a basis for telecom operators' accurate marketing; at the same time, it provides a reference direction for developing 6G research and tariff package development 4 .…”

Section: Introductionmentioning

confidence: 99%

Telecom 5G user prediction based on data mining and 6G server guidance

Jia

2023

Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023)

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With the accelerated digitization process, it is crucial to accurately identify potential 5G demand users through models to facilitate the transition from the 4G era to the 5G era in order to realize the construction of smart cities based on deep 5G applications. In order to accurately and efficiently identify 5G telecom users and analyze the business features that have important impacts on 5G users, this paper designs a 5G user prediction model combining k-means and classifier based on data mining. This model is based on k-means clustering to deal with the current dilemma of unbalanced sample size of 5G users and non-5G users, and additionally uses a bagging strategy to integrate two base classifiers, LightGBM and CatBoost, to effectively improve the model generalization capability. In comparison with the baseline model, the accuracy of the model is 87.7% and the F1-score is 70.4%. The results are satisfactory. With this model, 5G users can be identified with higher probability, which is a great help for mobile operators to identify potential users for accurate marketing. At the same time, the business characteristics highly correlated with 5G users also provide guidelines for 6G service design and package designation.

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A Transfer Learning Method for the Protection of Geographical Indication in China Using an Electronic Nose for the Identification of Xihu Longjing Tea

Wang

Liu

2021

IEEE Sensors J.

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Prediction of Tariff Package Model Using ROF-LGB Algorithm

Cited by 4 publications

References 5 publications

Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms

Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms

Telecom 5G user prediction based on data mining and 6G server guidance

A Transfer Learning Method for the Protection of Geographical Indication in China Using an Electronic Nose for the Identification of Xihu Longjing Tea

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