Publications

Publications by LIAAD

2021

Improving Smart Waste Collection Using AutoML

Authors
Teixeira, S; Londres, G; Veloso, B; Ribeiro, RP; Gama, J;

Publication
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, PT II

Abstract
The production and management of urban waste is a growing challenge and a consequence of our day-to-day resources and activities. According to the Portuguese Environment Agency, in 2019, Portugal produced 1% more tons compared to 2018. The proper management of this waste can be co-substantiated by existing policies, namely, national legislation and the Strategic Plan for Urban Waste. Those policies assess and support the amount of waste processed, allowing the recovery of materials. Among the solutions for waste management is the selective collection of waste. We improve the possibility of manage the smart waste collection of Paper, Plastic, and Glass packaging from corporate customers who joined a recycling program. We have data collected since 2017 until 2020. The main objective of this work is to increase the system's predictive performance, without any loss for citizens, but with improvement in the collection management. We analyze two types of problems: (i) the presence or absence of containers; and (ii) the prediction of the number of containers by type of waste. To carry out the analysis, we applied three machine learning algorithms: XGBoost, Random Forest, and Rpart. Additionally, we also use AutoML for XGBoost and Random Forest algorithms. The results show that with AutoML, generally, it is possible to obtain better results for classifying the presence or absence of containers by type of waste and predict the number of containers.

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2021

Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I

Authors
Kamp, M; Koprinska, I; Bibal, A; Bouadi, T; Frénay, B; Galárraga, L; Oramas, J; Adilova, L; Krishnamurthy, Y; Kang, B; Largeron, C; Lijffijt, J; Viard, T; Welke, P; Ruocco, M; Aune, E; Gallicchio, C; Schiele, G; Pernkopf, F; Blott, M; Fröning, H; Schindler, G; Guidotti, R; Monreale, A; Rinzivillo, S; Biecek, P; Ntoutsi, E; Pechenizkiy, M; Rosenhahn, B; Buckley, CL; Cialfi, D; Lanillos, P; Ramstead, M; Verbelen, T; Ferreira, PM; Andresini, G; Malerba, D; Medeiros, I; Viger, PF; Nawaz, MS; Ventura, S; Sun, M; Zhou, M; Bitetta, V; Bordino, I; Ferretti, A; Gullo, F; Ponti, G; Severini, L; Ribeiro, RP; Gama, J; Gavaldà, R; Cooper, LAD; Ghazaleh, N; Richiardi, J; Roqueiro, D; Miranda, DS; Sechidis, K; Graça, G;

Publication
PKDD/ECML Workshops (1)

Abstract

2021

Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II

Publication
PKDD/ECML Workshops (2)

Abstract

2021

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Authors
Kamp, M; Koprinska, I; Bibal, A; Bouadi, T; Frénay, B; Galárraga, L; Oramas, J; Adilova, L; Krishnamurthy, Y; Kang, B; Largeron, C; Lijffijt, J; Viard, T; Welke, P; Ruocco, M; Aune, E; Gallicchio, C; Schiele, G; Pernkopf, F; Blott, M; Fröning, H; Schindler, G; Guidotti, R; Monreale, A; Rinzivillo, S; Biecek, P; Ntoutsi, E; Pechenizkiy, M; Rosenhahn, B; Buckley, C; Cialfi, D; Lanillos, P; Ramstead, M; Verbelen, T; Ferreira, PM; Andresini, G; Malerba, D; Medeiros, I; Fournier-Viger, P; Nawaz, MS; Ventura, S; Sun, M; Zhou, M; Bitetta, V; Bordino, I; Ferretti, A; Gullo, F; Ponti, G; Severini, L; Ribeiro, R; Gama, J; Gavaldà, R; Cooper, L; Ghazaleh, N; Richiardi, J; Roqueiro, D; Saldana Miranda, D; Sechidis, K; Graça, G;

Publication
Communications in Computer and Information Science

Abstract

2021

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Authors
Kamp, M; Koprinska, I; Bibal, A; Bouadi, T; Frénay, B; Galárraga, L; Oramas, J; Adilova, L; Krishnamurthy, Y; Kang, B; Largeron, C; Lijffijt, J; Viard, T; Welke, P; Ruocco, M; Aune, E; Gallicchio, C; Schiele, G; Pernkopf, F; Blott, M; Fröning, H; Schindler, G; Guidotti, R; Monreale, A; Rinzivillo, S; Biecek, P; Ntoutsi, E; Pechenizkiy, M; Rosenhahn, B; Buckley, C; Cialfi, D; Lanillos, P; Ramstead, M; Verbelen, T; Ferreira, PM; Andresini, G; Malerba, D; Medeiros, I; Fournier-Viger, P; Nawaz, MS; Ventura, S; Sun, M; Zhou, M; Bitetta, V; Bordino, I; Ferretti, A; Gullo, F; Ponti, G; Severini, L; Ribeiro, R; Gama, J; Gavaldà, R; Cooper, L; Ghazaleh, N; Richiardi, J; Roqueiro, D; Saldana Miranda, D; Sechidis, K; Graça, G;

Publication
Communications in Computer and Information Science

Abstract

2021

Discriminant analysis of distributional data via fractional programming

Authors
Dias, S; Brito, P; Amaral, P;

Publication
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Abstract
We address classification of distributional data, where units are described by histogram or interval-valued variables. The proposed approach uses a linear discriminant function where distributions or intervals are represented by quantile functions, under specific assumptions. This discriminant function allows defining a score for each unit, in the form of a quantile function, which is used to classify the units in two a priori groups, using the Mallows distance. There is a diversity of application areas for the proposed linear discriminant method. In this work we classify the airline companies operating in NY airports based on air time and arrival/departure delays, using a full year flights.

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