2018
Authors
Branco, P; Torgo, L; Ribeiro, RP;
Publication
Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, LIDTA@ECML/PKDD 2018, Dublin, Ireland, September 10, 2018
Abstract
2015
Authors
Torgo, L; Branco, P; Ribeiro, RP; Pfahringer, B;
Publication
EXPERT SYSTEMS
Abstract
Several real world prediction problems involve forecasting rare values of a target variable. When this variable is nominal, we have a problem of class imbalance that was thoroughly studied within machine learning. For regression tasks, where the target variable is continuous, few works exist addressing this type of problem. Still, important applications involve forecasting rare extreme values of a continuous target variable. This paper describes a contribution to this type of tasks. Namely, we propose to address such tasks by resampling approaches that change the distribution of the given data set to decrease the problem of imbalance between the rare target cases and the most frequent ones. We present two modifications of well-known resampling strategies for classification tasks: the under-sampling and the synthetic minority over-sampling technique (SMOTE) methods. These modifications allow the use of these strategies on regression tasks where the goal is to forecast rare extreme values of the target variable. In an extensive set of experiments, we provide empirical evidence for the superiority of our proposals for these particular regression tasks. The proposed resampling methods can be used with any existing regression algorithm, which means that they are general tools for addressing problems of forecasting rare extreme values of a continuous target variable.
2019
Authors
Bellinger, C; Branco, P; Torgo, L;
Publication
Discovery Science - 22nd International Conference, DS 2019, Split, Croatia, October 28-30, 2019, Proceedings
Abstract
2019
Authors
Branco, P; Torgo, L;
Publication
2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Washington, DC, USA, October 5-8, 2019
Abstract
2019
Authors
Bellinger, C; Branco, P; Torgo, L;
Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
Addressing the class imbalance problem is critical for several real world applications. The application of pre-processing methods is a popular way of dealing with this problem. These solutions increase the rare class examples and/or decrease the normal class cases. However, these procedures typically only take into account the characteristics of each individual class. This segmented view of the data can have a negative impact. We propose a new method that uses an integrated view of the data classes to generate new examples and remove cases. ClUstered REsampling (CURE) is a method based on a holistic view of the data that uses hierarchical clustering and a new distance measure to guide the sampling procedure. Clusters generated in this way take into account the structure of the data. This enables CURE to avoid common mistakes made by other resampling methods. In particular, CURE prevents the generation of synthetic examples in dangerous regions and undersamples safe, non-borderline, regions of the majority class. We show the effectiveness of CURE in an extensive set of experiments with benchmark domains. We also show that CURE is a user-friendly method that does not require extensive fine-tuning of hyper-parameters. © Springer Nature Switzerland AG 2019.
2020
Authors
Wu, M; Branco, P; Chen Ke, JX; MacDonald, DB;
Publication
IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Virtual Event, South Korea, December 16-19, 2020
Abstract
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