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2017 | OriginalPaper | Buchkapitel

Discovering Minority Sub-clusters and Local Difficulty Factors from Imbalanced Data

verfasst von : Mateusz Lango, Dariusz Brzezinski, Sebastian Firlik, Jerzy Stefanowski

Erschienen in: Discovery Science

Verlag: Springer International Publishing

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Abstract

Learning classifiers from imbalanced data is particularly challenging when class imbalance is accompanied by local data difficulty factors, such as outliers, rare cases, class overlapping, or minority class decomposition. Although these issues have been highlighted in previous research, there have been no proposals of algorithms that simultaneously detect all the aforementioned difficulties in a dataset. In this paper, we put forward two extensions to popular clustering algorithms, ImKmeans and ImScan, and one novel algorithm, ImGrid, that attempt to detect minority sub-clusters, outliers, rare cases, and class overlapping. Experiments with artificial datasets show that ImGrid, which uses a Bayesian test to join similar neighboring regions, is able to re-discover simulated clusters and types of minority examples on par with competing methods, while being the least sensitive to parameter tuning.

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Vorheriges Kapitel Measuring the Inspiration Rate of Topics in Bibliographic Networks

Nächstes Kapitel Fusion Techniques for Named Entity Recognition and Word Sense Induction and Disambiguation

Details on tuning the size of the neighborhood and a comparison between the k-NN and kernel-based approach can be found in [12].

Source code, datasets, and reproducible test scripts available at: https://github.com/langus0/imgrid.

http://www.cs.put.poznan.pl/dbrzezinski/software/MinorityAnalysis.html.

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Titel: Discovering Minority Sub-clusters and Local Difficulty Factors from Imbalanced Data
verfasst von: Mateusz Lango
Dariusz Brzezinski
Sebastian Firlik
Jerzy Stefanowski
Verlag: Springer International Publishing
Buch: Discovery Science
Print ISBN: 978-3-319-67785-9

Electronic ISBN: 978-3-319-67786-6

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-67786-6_23

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