Abstract
One of the many application areas for machine learning tools is waste management, where mainly classification of different waste materials and prediction of waste generation can be found. The conducted literature review indicated that there is a lack of papers concerning waste-related text classification.
In this research, we investigated the automatic text classification of complaint reports written in Polish that were sent to the municipal waste management system operating in one of the biggest Polish cities, Wroclaw. The analyzed problem regards a multi-class Machine Learning classification. Waste-related vocabulary, Polish language, and unlabeled dataset are the main difficulties in the considered problem.
The results showed that the automatic classification using Machine Learning algorithms achieved higher accuracy than the standard procedure based on people’s choices currently applied in the reporting system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kannangara, M., Dua, R., Ahmadi, L., Bensebaa, F.: Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches. Waste Manag. 74, 3–15 (2018)
Abbasi, M., El Hanandeh, A.: Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Manag. 56, 13–22 (2016)
Kontokosta, C.E., Hong, B., Johnson, N.E., Starobin, D.: Using machine learning and small area estimation to predict building-level municipal solid waste generation in cities. Comput. Environ. Urban Syst. 70, 151–162 (2018)
Rutqvist, D., Kleyko, D., Blomstedt, F.: An automated machine learning approach for smart waste management systems. IEEE Trans. Ind. Inf. 16(1), 384–392 (2020)
Ferrer, J., Alba, E.: BIN-CT: Urban waste collection based on predicting the container fill level. BioSystems 186, 103962 (2019)
Khoa, T.A., Phuc, C.H., Lam, P.D., Mai, L., Nhu, B., Trong, N.M., Thi, N., Phuong, H., Dung, N., Van, T.-Y.N., Nguyen, H.N., Ngoc, D., Duc, M.: Waste management system using IoT-based machine learning in University. Wirel. Commun. Mob. Comput. 2020, 1–13 (2020)
Solano Meza, J.K., Orjuela Yepes, D., Rodrigo-Ilarri, J., Cassiraga, E.: Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks. Heliyon 5(11), e02810 (2019)
Niska, H., Serkkola, A.: Data analytics approach to create waste generation profiles for waste management and collection. Waste Manag. 77, 477–485 (2018)
Vu, D.D., Kaddoum, G.: A waste city management system for smart cities applications. In: 2017 Advances in Wireless and Optical Communications, RTUWO 2017, pp. 225–229 (2017)
Adedeji, O., Wang, Z.: Intelligent waste classification system using deep learning convolutional neural network. Procedia Manuf. 35, 607–612 (2019)
Tarun, K., Sreelakshmi, K., Peeyush, K.P.: Segregation of plastic and non-plastic waste using convolutional neural network. In: IOP Conference Series: Materials Science and Engineering, vol. 561, no. 1, p. 012113 (2019)
Sousa, J., Rebelo, A., Cardoso, J.S.: Automation of Waste Sorting with Deep Learning. In: Proceedings - 15th Workshop of Computer Vision, WVC 2019, pp. 43–48 (2019).
John, N.E., Sreelakshmi, R., Menon, S.R., Santhosh, V.: Artificial neural network based intelligent waste segregator. Int. J. Sci. Eng. Res. 10(4), 367–370 (2019)
Walkowiak, T., Malak, P.: Polish texts topic classification evaluation. In: ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence, vol. 2, pp. 515–522 (2018)
Dzisevic, R., Sesok, D.: Text classification using different feature extraction approaches. In: 2019 Open Conference of Electrical, Electronic and Information Sciences, eStream 2019 – Proceedings, pp. 1–4 (2019)
Kadhim, A.I.: Survey on supervised machine learning techniques for automatic text classification. Artif. Intell. Rev. 52(1), 273–292 (2019)
Kowsari, K., Meimandi, K.J., Heidarysafa, M., Mendu, S., Barnes, L., Brown, D.: Text classification algorithms: a survey. Information (Switzerland) 10(4), 1–68 (2019)
Hossin, M., Sulaiman, M.N.: A Review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag Process 5(2), 01–11 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Dąbrowska, A., Giel, R., Werbińska-Wojciechowska, S. (2021). Automatic Multi-class Classification of Polish Complaint Reports About Municipal Waste Management. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) Theory and Engineering of Dependable Computer Systems and Networks. DepCoS-RELCOMEX 2021. Advances in Intelligent Systems and Computing, vol 1389. Springer, Cham. https://doi.org/10.1007/978-3-030-76773-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-76773-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-76772-3
Online ISBN: 978-3-030-76773-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)