Publications

Publications by LIAAD

2021

Chebyshev approaches for imbalanced data streams regression models

Authors
Aminian, E; Ribeiro, RP; Gama, J;

Publication
DATA MINING AND KNOWLEDGE DISCOVERY

Abstract
In recent years data stream mining and learning from imbalanced data have been active research areas. Even though solutions exist to tackle these two problems, most of them are not designed to handle challenges inherited from both problems. As far as we are aware, the few approaches in the area of learning from imbalanced data streams fall in the context of classification, and no efforts on the regression domain have been reported yet. This paper proposes a technique that uses sampling strategies to cope with imbalanced data streams in a regression setting, where the most important cases have rare and extreme target values. Specifically, we employ under-sampling and over-sampling strategies that resort to Chebyshev's inequality value as a heuristic to disclose the type of incoming cases (i.e. frequent or rare). We have evaluated our proposal by applying it in the training of models by four well-known regression algorithms over fourteen benchmark data sets. We conducted a series of experiments with different setups on both synthetic and real-world data sets. The experimental results confirm our approach's effectiveness by showing the models' superior performance trained by each of the sampling strategies compared with their baseline pairs.

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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