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Progress in Artificial Intelligence

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06.02.2019 | Regular Paper

Instance selection improves geometric mean accuracy: a study on imbalanced data classification

verfasst von: Ludmila I. Kuncheva, Álvar Arnaiz-González, José-Francisco Díez-Pastor, Iain A. D. Gunn

Erschienen in: Progress in Artificial Intelligence | Ausgabe 2/2019

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Abstract

A natural way of handling imbalanced data is to attempt to equalise the class frequencies and train the classifier of choice on balanced data. For two-class imbalanced problems, the classification success is typically measured by the geometric mean (GM) of the true positive and true negative rates. Here we prove that GM can be improved upon by instance selection, and give the theoretical conditions for such an improvement. We demonstrate that GM is non-monotonic with respect to the number of retained instances, which discourages systematic instance selection. We also show that balancing the distribution frequencies is inferior to a direct maximisation of GM. To verify our theoretical findings, we carried out an experimental study of 12 instance selection methods for imbalanced data, using 66 standard benchmark data sets. The results reveal possible room for new instance selection methods for imbalanced data.

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We find it curious that no such methods, on this category, have yet been developed to maximise GM.

Available at http://sci2s.ugr.es/keel/imbalanced.php.

We noticed that, while the original OSS is defined by Kubat in [30] as CNN followed by TL, later on, Batista [5] defined it in reverse order and also independently proposed an equivalent to Kubat’s OSS. This misunderstanding has spread in subsequent works. However, we have maintained the original name OSS for CNN+TL, as used [30], and we use TL+CNN for Batista et al.’s method [5].

The random selection was performed by using the SpreadSubsample instance supervised filter.

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Available in Google code: https://code.google.com/archive/p/imbalanced-data-sampling/.

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Titel: Instance selection improves geometric mean accuracy: a study on imbalanced data classification
verfasst von: Ludmila I. Kuncheva
Álvar Arnaiz-González
José-Francisco Díez-Pastor
Iain A. D. Gunn
Publikationsdatum: 06.02.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Progress in Artificial Intelligence / Ausgabe 2/2019
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI: https://doi.org/10.1007/s13748-019-00172-4

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