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Grid-Search Enhanced Tree-Based Machine Learning for Traffic IoT Data Anomaly Detection

Li, Shuai (author)
University of Jinan, CHN
Sun, Bin, 1988- (author)
Blekinge Tekniska Högskola,Institutionen för datavetenskap
Geng, Rrenkang (author)
University of Jinan, CHN
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Zhang, Lu (author)
University of Jinan, CHN
Shen, Tao (author)
University of Jinan, CHN
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 (creator_code:org_t)
2022-10-20
2022
English.
In: Proceedings of the 12th International Conference on Computer Engineering and Networks. - Singapore : Springer Science+Business Media B.V.. - 9789811969003 ; , s. 3-9
  • Conference paper (peer-reviewed)
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

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Li, Shuai
Sun, Bin, 1988-
Geng, Rrenkang
Zhang, Lu
Shen, Tao
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NATURAL SCIENCES
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Blekinge Institute of Technology

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