A Genetic-Based Ensemble Learning Applied to Imbalanced Data Classification
- 538 Downloads
Imbalanced data classification is still a focus of intense research, due to its ever-growing presence in the real-life decision tasks. In this article, we focus on a classifier ensemble for imbalanced data classification. The ensemble is formed on the basis of the individual classifiers trained on supervise-selected feature subsets. There are several methods employing this concept to ensure a high diverse ensemble, nevertheless most of them, as Random Subspace or Random Forest, select attributes for a particular classifier randomly. The main drawback of mentioned methods is not giving the ability to supervise and control this task. In following work, we apply a genetic algorithm to the considered problem. Proposition formulates an original learning criterion, taking into consideration not only the overall classification performance but also ensures that trained ensemble is characterised by high diversity. The experimental study confirmed the high efficiency of the proposed algorithm and its superiority to other ensemble forming method based on random feature selection.
KeywordsMachine learning Classification Imbalanced data Feature selection Genetic algorithm
This work was supported by the Polish National Science Centre under the grant No. 2017/27/B/ST6/01325 as well as by the statutory funds of the Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Science and Technology.
- 1.Alcalá-Fdez, J., et al.: Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Mult. Valued Log. Soft Comput. 17, 255–287 (2011)Google Scholar
- 3.Branco, P., Torgo, L., Ribeiro, R.P.: Relevance-based evaluation metrics for multi-class imbalanced domains. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 698–710. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57454-7_54CrossRefGoogle Scholar
- 4.Canuto, A.M., Nascimento, D.S.: A genetic-based approach to features selection for ensembles using a hybrid and adaptive fitness function. In: The 2012 international joint conference on neural networks (IJCNN), pp. 1–8. IEEE (2012)Google Scholar
- 6.Du, L., Xu, Y., Jin, L.: Feature selection for imbalanced datasets based on improved genetic algorithm. In: Decision Making and Soft Computing: Proceedings of the 11th International FLINS Conference, pp. 119–124. World Scientific (2014)Google Scholar
- 11.Ksieniewicz, P., Woźniak, M.: Imbalanced data classification based on feature selection techniques. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A.J. (eds.) IDEAL 2018. LNCS, vol. 11315, pp. 296–303. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03496-2_33CrossRefGoogle Scholar