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Grid-Search Enhance...
Grid-Search Enhanced Tree-Based Machine Learning for Traffic IoT Data Anomaly Detection
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- Li, Shuai (author)
- University of Jinan, CHN
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- Sun, Bin, 1988- (author)
- Blekinge Tekniska Högskola,Institutionen för datavetenskap
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- Geng, Rrenkang (author)
- University of Jinan, CHN
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- Zhang, Lu (author)
- University of Jinan, CHN
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- Shen, Tao (author)
- University of Jinan, CHN
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(creator_code:org_t)
- 2022-10-20
- 2022
- English.
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In: Proceedings of the 12th International Conference on Computer Engineering and Networks. - Singapore : Springer Science+Business Media B.V.. - 9789811969003 ; , s. 3-9
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Abstract
Subject headings
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- Anomaly detection is an important part of machine learning. Detection of outliers in the field of transportation can provide valid data for future traffic predictions or traffic flow analysis. This paper builds a model based on XGBoost to detect outliers in IoT data. The data is preprocessed first, followed by model building. Then we use the grid search to adjust the parameters and substitute the optimal parameters into the building model. To validate the model, we cross-checked it with two benchmark models, iFroset and Random Forest. The final experimental results show that the model constructed in this paper can accurately detect outliers in traffic flow and the accuracy is better than that of the baseline model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Anomaly detection
- Traffic flow
- XGBoost
- Internet of things
- Machine learning
- Statistics
- Data anomalies
- Grid search
- Machine-learning
- Model-based OPC
- Traffic flow analysis
- Traffic prediction
- Tree-based
Publication and Content Type
- ref (subject category)
- kon (subject category)
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