An air quality anomaly detection method for data imbalance distribution

By constructing an HPO-LGBM model and combining random forest feature selection and stratified sampling rebalancing methods to optimize model parameters, the problems of overfitting and information loss caused by data imbalance in air quality anomaly detection are solved, achieving more efficient air quality anomaly detection.

CN117076976BActive Publication Date: 2026-07-21BEIJING UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
BEIJING UNIV OF TECH
Filing Date
2023-07-27
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing air quality anomaly detection models suffer from overfitting and classifiers missing important information when dealing with imbalanced data distributions. Furthermore, existing rebalancing methods are susceptible to noise and have long training times, resulting in poor model performance.

Method used

The HPO-LGBM model was adopted, and combined with air quality monitoring station data and abnormal complaint data. The HPO-LGBM model was constructed by random forest feature selection and stratified sampling rebalancing method. The model parameters were optimized by random forest Bayesian hyperparameter optimization algorithm to achieve data rebalancing and model performance improvement.

Benefits of technology

It improves the accuracy and efficiency of air quality anomaly detection, especially outperforming other models in terms of F1, AUC, G-mean, and MCC values, solving the data imbalance problem and enhancing the model's generalization ability.

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Abstract

The application discloses a kind of fusion integrated air quality anomaly detection methods for data imbalance distribution, which is used to realize air quality anomaly detection for data imbalance distribution.Firstly, the method extracts air quality anomaly data in combination with air quality monitoring data collected by air quality detection site, air quality anomaly complaint corpus data, establishes air quality anomaly detection dataset;Secondly, a random forest feature selection method (ParcelForestModel) is used to construct air quality anomaly detection dataset feature vector;Thirdly, a data rebalancing method based on stratified sampling is proposed;Finally, a fusion integrated HPO-LGBM model is constructed based on the Bayesian hyperparameter optimization algorithm of random forest, and air quality anomaly detection is realized.The application constructs HPO-LGBM model, overcomes limiting factors, and provides an open research framework for air quality anomaly detection for data imbalance distribution.
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