An Automated Machine Learning Approach for Smart Waste Management Systems

This paper presents the use of automated machine learning for solving a practical problem of a real-life Smart Waste Management system. In particular, the focus of the paper is on the problem of detection (i.e., binary classification) of emptying of a recycling container using sensor measurements. N...

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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 16; H. 1; S. 384 - 392
Hauptverfasser: Rutqvist, David, Kleyko, Denis, Blomstedt, Fredrik
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accelerometers
Acoustics
Algorithms
Automated machine learning (AutoML)
Automated machines
Automation
Binary classification
Classification
Classification (of information)
Classification accuracy
Classification algorithm
classification algorithms
Containers
Conventional machines
data mining
Decision trees
Dependable Communication and Computation Systems
Emptying
emptying detection
grid search
Informatics
Kommunikations- och beräkningssystem
Learning algorithms
Machine learning
Management systems
Measurement methods
Random forest classifier
Recycling
Smart Waste Management
Ultrasonic variables measurement
Waste management
Waste management systems
ISSN:1551-3203, 1941-0050, 1941-0050
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Beschreibung
Zusammenfassung:This paper presents the use of automated machine learning for solving a practical problem of a real-life Smart Waste Management system. In particular, the focus of the paper is on the problem of detection (i.e., binary classification) of emptying of a recycling container using sensor measurements. Numerous data-driven methods for solving the problem are investigated in a realistic setting where most of the events are not actual emptying. The investigated methods include the existing manually engineered model and its modification as well as conventional machines learning algorithms. The use of machine learning allows improving the classification accuracy and recall of the existing manually engineered model from 86.8% and 47.9% to 99.1% and 98.2%, respectively, when using the best performing solution. This solution uses a Random Forest classifier on a set of features based on the filling level at different given time spans. Finally, compared to the baseline existing manually engineered model, the best performing solution also improves the quality of forecasts for emptying time of recycling containers.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1551-3203
1941-0050
1941-0050
DOI:10.1109/TII.2019.2915572