An XCS-Based Algorithm for Classifying Imbalanced Datasets

Hooman Sanatkar; Saman Haratizadeh

doi:doi:10.11648/j.ijiis.20150406.12

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An XCS-Based Algorithm for Classifying Imbalanced Datasets

Hooman Sanatkar, Saman Haratizadeh

Published in International Journal of Intelligent Information Systems (Volume 4, Issue 6)

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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[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.
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[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.
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[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
PY  - 2015
N1  - https://doi.org/10.11648/j.ijiis.20150406.12
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
SP  - 101
EP  - 105
PB  - Science Publishing Group
SN  - 2328-7683
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

Hooman Sanatkar

Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Saman Haratizadeh

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

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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}
}

Copy | Download

TY  - JOUR
T1  - An XCS-Based Algorithm for Classifying Imbalanced Datasets
AU  - Hooman Sanatkar
AU  - Saman Haratizadeh
Y1  - 2015/12/14
PY  - 2015
N1  - https://doi.org/10.11648/j.ijiis.20150406.12
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
SP  - 101
EP  - 105
PB  - Science Publishing Group
SN  - 2328-7683
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  -

Copy | Download