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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| Vydáno v: | IEEE transactions on industrial informatics Ročník 16; číslo 1; s. 384 - 392 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Piscataway
IEEE
01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1551-3203, 1941-0050, 1941-0050 |
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| Abstract | 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. |
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| AbstractList | 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. 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. 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 [Formula Omitted] and [Formula Omitted] to [Formula Omitted] and [Formula Omitted], 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. |
| Author | Blomstedt, Fredrik Kleyko, Denis Rutqvist, David |
| Author_xml | – sequence: 1 givenname: David surname: Rutqvist fullname: Rutqvist, David email: david.rutqvist@bnearit.se organization: BnearIT AB, Luleå, Sweden – sequence: 2 givenname: Denis orcidid: 0000-0002-6032-6155 surname: Kleyko fullname: Kleyko, Denis email: denis.kleyko@ltu.se organization: Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden – sequence: 3 givenname: Fredrik surname: Blomstedt fullname: Blomstedt, Fredrik email: fredrik.blomstedt@bnearit.se organization: BnearIT AB, Luleå, Sweden |
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| SubjectTerms | 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 |
| Title | An Automated Machine Learning Approach for Smart Waste Management Systems |
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