Learning on the border: Active learning in imbalanced data classification

Date

2007-10-06

Author

Ertekin Bolelli, Şeyda
Bottou, Leon
Giles, C Lee

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper is concerned with the class imbalance problem which has been known to hinder the learning performance of classification algorithms. The problem occurs when there are significantly less number of observations of the target concept. Various real-world classification tasks, such as medical diagnosis, text categorization and fraud detection suffer from this phenomenon. The standard machine learning algorithms yield better prediction performance with balanced datasets. In this paper, we demonstrate that active learning is capable of solving the class imbalance problem by providing the learner more balanced classes. We also propose an efficient way of selecting informative instances from a smaller pool of samples for active learning which does not necessitate a search through the entire dataset. The proposed method yields an efficient querying system and allows active learning to be applied to very large datasets. Our experimental results show that with an early stopping criteria, active learning achieves a fast solution with competitive prediction performance in imbalanced data classification.

URI

https://hdl.handle.net/11511/69499

DOI

https://doi.org/10.1145/1321440.1321461

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

Ş. Ertekin Bolelli, L. Bottou, and C. L. Giles, “Learning on the border: Active learning in imbalanced data classification,” 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/69499.