| Peer-Reviewed

An XCS-Based Algorithm for Classifying Imbalanced Datasets

Received: 5 November 2015     Accepted: 22 November 2015     Published: 14 December 2015
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Abstract

Imbalanced datasets are datasets with different samples distribution in which the distribution of samples in one class is scientifically more than other class samples. Learning a classification model for such imbalanced data has been shown to be a tricky task. In this paper we will focus on learning classifier systems, and will suggest a new XCS-based approach for learning classification models from imbalanced data sets. The main idea behind the suggested approach is to update the important parameters of the learning method based on the information gathered in each step of learning, in order to provide a fair situation for the minor class, to contribute in building the final model. We have also evaluated our approach by testing it with real-world known imbalanced datasets. The results show that our new algorithm has a high detection rate and a low false positive rate.

Published in International Journal of Intelligent Information Systems (Volume 4, Issue 6)
DOI 10.11648/j.ijiis.20150406.12
Page(s) 101-105
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Imbalanced Dataset, Evolutionary Algorithm, XCS

References
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[2] Xue, Jing-Hao and P. Hall, "Why Does Rebalancing Class-unbalanced Data Improve AUC for Linear Discriminant Analysis," Pattern Analysis and Machine Intelligence,IEEE Transactions on , pp. 1109-1112, 2015.
[3] M. Butz, "Learning classifier systems," in Springer Handbook of Computational Intelligence, Berlin , Springer , 2015, pp. 961-981.
[4] P. L. Lanzi, "Learning classifier systems: a gentle introduction," in companion on Genetic and evolutionary computation companion, 2014.
[5] L. Bull, "A brief history of learning classifier systems: from CS-1 to XCS and its variants," Evolutionary Intelligence, 2015.
[6] N. CHAWLA, K. Bowyer, L. Hall and W. Kegelmeye, "SMOTE: Synthetic minority over-sampling technique," Journal of Artificial Intelligence Research, vol. 15, pp. 321-357, 2002.
[7] N. Japcowicz and S. Stephen, "The Class Imbalance Problem:A Systematic Study," Intelligent Data Analisis, 2002.
[8] Wang, Shuo, L. Leandro, Minku and Xin Ya, "Resampling-Based Ensemble Methods for Online Class Imbalance Learning," Knowledge and Data Engineering, IEEE Transactions on, pp. 1356-1368, 2015.
[9] A. Orriols-Puig, "Facetwise Analysis of Learning Classifier Systems in Imbalanced Domains," Ramon Liull University, 2006.
[10] A. Orriols-Puig and E. Bernad´o-Mansilla, "Evolutionary rule-based systems for imbalanced datasets," Soft Computing Journal, vol. 13, no. 3, pp. 213-225, 2009.
[11] A. Orriols-Puig and E. Bernad´o-Mansilla, "Bounding XCS parameters for unbalanced datasets," in Genetic and Evolutionary Computation Conference, 2006.
[12] A. Orriols-Puig and E. Bernad´o-Mansilla, "The Class Imbalance Problem in Learning Classifier Systems:A Preliminary Study," in Genetic and Evolutionary Computation Conference, 2005.
[13] K. Deb, A. Pratap , S. Agarwal and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II.," Evolutionary Computation, vol. 6, no. 2, pp. 182-197, 2002.
[14] M. Erickson, A. Mayer and J. Horn, "Multi-objective optimal design of groundwater remediation systems: application of the niched Pareto genetic algorithm (NPGA)," Advances in Water Resources, vol. 25, no. 1, pp. 51-65, 2002.
[15] D. Corne, N. R. Jerram, J. D. Knowles and M. J. Oates, "Corne, David W., et al. "PESA-II: Region-based selection in evolutionary multiobjective optimization," in Genetic and Evolutionary Computation Conference, 2001.
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  • APA Style

    Hooman Sanatkar, Saman Haratizadeh. (2015). An XCS-Based Algorithm for Classifying Imbalanced Datasets. International Journal of Intelligent Information Systems, 4(6), 101-105. https://doi.org/10.11648/j.ijiis.20150406.12

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    ACS Style

    Hooman Sanatkar; Saman Haratizadeh. An XCS-Based Algorithm for Classifying Imbalanced Datasets. Int. J. Intell. Inf. Syst. 2015, 4(6), 101-105. doi: 10.11648/j.ijiis.20150406.12

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    AMA Style

    Hooman Sanatkar, Saman Haratizadeh. An XCS-Based Algorithm for Classifying Imbalanced Datasets. Int J Intell Inf Syst. 2015;4(6):101-105. doi: 10.11648/j.ijiis.20150406.12

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  • @article{10.11648/j.ijiis.20150406.12,
      author = {Hooman Sanatkar and Saman Haratizadeh},
      title = {An XCS-Based Algorithm for Classifying Imbalanced Datasets},
      journal = {International Journal of Intelligent Information Systems},
      volume = {4},
      number = {6},
      pages = {101-105},
      doi = {10.11648/j.ijiis.20150406.12},
      url = {https://doi.org/10.11648/j.ijiis.20150406.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20150406.12},
      abstract = {Imbalanced datasets are datasets with different samples distribution in which the distribution of samples in one class is scientifically more than other class samples. Learning a classification model for such imbalanced data has been shown to be a tricky task. In this paper we will focus on learning classifier systems, and will suggest a new XCS-based approach for learning classification models from imbalanced data sets. The main idea behind the suggested approach is to update the important parameters of the learning method based on the information gathered in each step of learning, in order to provide a fair situation for the minor class, to contribute in building the final model. We have also evaluated our approach by testing it with real-world known imbalanced datasets. The results show that our new algorithm has a high detection rate and a low false positive rate.},
     year = {2015}
    }
    

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  • TY  - JOUR
    T1  - An XCS-Based Algorithm for Classifying Imbalanced Datasets
    AU  - Hooman Sanatkar
    AU  - Saman Haratizadeh
    Y1  - 2015/12/14
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    DO  - 10.11648/j.ijiis.20150406.12
    T2  - International Journal of Intelligent Information Systems
    JF  - International Journal of Intelligent Information Systems
    JO  - International Journal of Intelligent Information Systems
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.ijiis.20150406.12
    AB  - Imbalanced datasets are datasets with different samples distribution in which the distribution of samples in one class is scientifically more than other class samples. Learning a classification model for such imbalanced data has been shown to be a tricky task. In this paper we will focus on learning classifier systems, and will suggest a new XCS-based approach for learning classification models from imbalanced data sets. The main idea behind the suggested approach is to update the important parameters of the learning method based on the information gathered in each step of learning, in order to provide a fair situation for the minor class, to contribute in building the final model. We have also evaluated our approach by testing it with real-world known imbalanced datasets. The results show that our new algorithm has a high detection rate and a low false positive rate.
    VL  - 4
    IS  - 6
    ER  - 

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Author Information
  • Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran

  • Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran

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