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Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE
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Orientador(es)
Resumo(s)
Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem, methods which generate artificial data to achieve a balanced class distribution are more versatile than modifications to the classification algorithm. Such techniques, called oversamplers, modify the training data, allowing any classifier to be used with class-imbalanced datasets. Many algorithms have been proposed for this task, but most are complex and tend to generate unnecessary noise. This work presents a simple and effective oversampling method based on k-means clustering and SMOTE (synthetic minority oversampling technique), which avoids the generation of noise and effectively overcomes imbalances between and within classes. Empirical results of extensive experiments with 90 datasets show that training data oversampled with the proposed method improves classification results. Moreover, k-means SMOTE consistently outperforms other popular oversampling methods. An implementation1 is made available in the Python programming language.
Descrição
Douzas, G., Bação, F., & Last, F. (2018). Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Information Sciences, 465, 1-20. DOI: 10.1016/j.ins.2018.06.056
Palavras-chave
Class-imbalanced learning Classification Clustering Oversampling Supervised learning Within-class imbalance Software Control and Systems Engineering Theoretical Computer Science Computer Science Applications Information Systems and Management Artificial Intelligence