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Evolutionary Cost-Sensitive Balancing: A Generic Method for Imbalanced Classification Problems

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EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 674))

Abstract

Efficient classification under imbalanced class distributions is currently of interest in data mining research, considering that traditional learning methods often fail to achieve satisfying results in such domains. Also, the correct choice of the metric is essential for the recognition effort. This paper presents a new general methodology for improving the performance of classifiers in imbalanced problems. The method, Evolutionary Cost-Sensitive Balancing (ECSB), is a meta-approach, which can be employed with any error-reduction classifier. It utilizes genetic search and cost-sensitive mechanisms to boost the performance of the base classifier. We present evaluations on benchmark data, comparing the results obtained by ECSB with those of similar recent methods in the literature: SMOTE and EUS. We found that ECSB boosts the performance of traditional classifiers in imbalanced problems, achieving ~45% relative improvement in true positive rate (\(\text {TP}_{\text {rate}}\)) and around 16% in F-measure (FM) on the average; also, it performs better than sampling strategies, with ~35% relative improvement in \(\text {TP}_{\text {rate}}\) and ~12% in FM over SMOTE (on the average), similar \(text{TP}_{\text {rate}}\) and geometric mean (GM) values and slightly higher area under de curve (AUC) values than EUS (up to ~9% relative improvement).

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Acknowledgement

The work of the authors is supported by European Social Fund, via Programme POSDRU, DMI 1.5, ID 137516 – PARTING

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Authors and Affiliations

  1. Computer Science Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

    Camelia Lemnaru & Rodica Potolea

Authors
  1. Camelia Lemnaru
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  2. Rodica Potolea
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Corresponding author

Correspondence to Camelia Lemnaru .

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Editors and Affiliations

  1. Computer Science and Communications Research Unit, University of Luxembourg E009 (CSC) Kirchberg Campus, Luxembourg, Luxembourg

    Alexandru-Adrian Tantar

  2. Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg

    Emilia Tantar

  3. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Michael Emmerich

  4. Bâtiment Leyteire, URF Sciences et Modelisation, Université Bordeaux, Bordeaux, France

    Pierrick Legrand

  5. Faculty of Computer Science, Alexandru Ioan Cuza University of Iași, Iași, Romania

    Lenuta Alboaie

  6. Faculty of Computer Science, Alexandru Ioan Cuza University, Iasi, Romania

    Henri Luchian

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Lemnaru, C., Potolea, R. (2018). Evolutionary Cost-Sensitive Balancing: A Generic Method for Imbalanced Classification Problems. In: Tantar, AA., Tantar, E., Emmerich, M., Legrand, P., Alboaie, L., Luchian, H. (eds) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI. Advances in Intelligent Systems and Computing, vol 674. Springer, Cham. https://doi.org/10.1007/978-3-319-69710-9_14

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  • DOI: https://doi.org/10.1007/978-3-319-69710-9_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69708-6

  • Online ISBN: 978-3-319-69710-9

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