An XCS-Based Algorithm for Classifying Imbalanced Datasets
International Journal of Intelligent Information Systems
Volume 4, Issue 6, December 2015, Pages: 101-105
Received: Nov. 5, 2015; Accepted: Nov. 22, 2015; Published: Dec. 14, 2015
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Authors
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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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.
Keywords
Imbalanced Dataset, Evolutionary Algorithm, XCS
To cite this article
Hooman Sanatkar, Saman Haratizadeh, An XCS-Based Algorithm for Classifying Imbalanced Datasets, International Journal of Intelligent Information Systems. Vol. 4, No. 6, 2015, pp. 101-105. doi: 10.11648/j.ijiis.20150406.12
Copyright
Copyright © 2015 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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