Feature Selection in Imbalanced Data

Kamalov, Firuz
Thabtah, Fadi
Leung, Ho Hon
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Springer Science and Business Media Deutschland GmbH
The traditional feature selection methods are not suitable for imbalanced data as they tend to be biased towards the majority class. This problem is particularly acute in the field of medical diagnostics and fraud detection where the class distribution is highly skewed. In this paper, we propose a novel filter approach using decision tree-based F1-score. The F1-score incorporates the accuracy with respect to the minority class data and hence is a good measure in the case of imbalanced data. In the proposed implementation, the F1-score is calculated based on a 1-dimensional decision tree classifier resulting in a fast and effective feature evaluation method. Numerical experiments confirm that the proposed method achieves robust dimensionality reduction and accuracy results. In addition, the low computational complexity of the algorithm makes it a practical choice for big data applications. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
This article is not available at CUD collection. The version of scholarly record of this article is published in Annals of Data Science (2022), available online at: https://doi.org/10.1007/s40745-021-00366-5
Big data, Data mining, F1-score, Feature selection, Filter method, Imbalanced data, Machine learning
Kamalov, F., Thabtah, F., & Leung, H. H. (2022). Feature selection in imbalanced data. Annals of Data Science, https://doi.org/10.1007/s40745-021-00366-5