Classification of imbalanced data remains an important field in machine learning. Several methods have been proposed to address the class imbalance problem including data resampling, adaptive learning and cost adjusting algorithms. Data resampling methods are widely used due to their simplicity and flexibility. Most existing resampling techniques aim at rebalancing class distribution. However, class imbalance is not the only factor that impacts the performance of the learning algorithm. Class overlap has proved to have a higher impact on the classification of imbalanced datasets than the dominance of the negative class. In this paper, we propose a new undersampling method that eliminates negative instances from the overlapping region and hence improves the visibility of the minority instances. Testing and evaluating the proposed method using 36 public imbalanced datasets showed statistically significant improvements in classification performance.
|Title of host publication||Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings|
|Editors||Hujun Yin, Paulo Novais, David Camacho, Antonio J. Tallón-Ballesteros|
|Number of pages||9|
|Publication status||Published - 2018|
|Event||19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 - Madrid, Spain|
Duration: 21 Nov 2018 → 23 Nov 2018
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018|
|Period||21/11/18 → 23/11/18|
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