A hybrid machine learning method and its application in municipal waste prediction

Authors:
Emadoddin Livani;Raymond Nguyen;Jörg Denzinger;Günther Ruhe;Scott Banack
Affiliations:
Department of Electrical and Computer Engineering, University of Calgary, Canada;David R. Cheriton School of Computer Science, University of Waterloo, Canada;Department of Computer Science, University of Calgary, Canada;Department of Electrical and Computer Engineering, University of Calgary, Canada,Department of Computer Science, University of Calgary, Canada;Waste and Recycling Services, City of Calgary, Canada
Venue:
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Year:
2013

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Abstract

Prediction methods combining clustering and classification techniques have the potential of creating more accurate results than the individual techniques, particularly for large datasets. In this paper, a hybrid prediction method is proposed from combining weighted k-means clustering and linear regression. Weighted k-means is used to cluster the dataset. Then, linear regression is performed on each cluster to build the final predictors. The proposed method has been applied to the problem of municipal waste prediction and evaluated with a dataset including 63,000 records. The results showed that it outperforms the single application of linear regression and k-means clustering in terms of prediction accuracy and robustness. The prediction model is integrated into a decision support system for strategic and operational planning of waste and recycling services at the City of Calgary in Canada. The potential usage of the prediction model is to improve the resource utilization, like personnel and vehicles.