Abstract
Instance selection, often referred to as data reduction, aims at deciding which instances from the training set should be retained for further use during the learning process. Instance selection is the important preprocessing step for many machine leaning tools, especially when the huge data sets are considered. Class imbalance arises, when the number of examples belonging to one class is much greater than the number of examples belonging to another. The paper proposes a cluster-based instance selection approach for the imbalanced data classification. The proposed approach bases on the similarity coefficient between training data instances, calculated for each considered data class independently. Similar instances are grouped into clusters. Next, the instance selection is carried out. The process of instance selection is controlled and carried-out by the team of agents. The proposed approach is validated experimentally. Advantages and main features of the approach are discussed considering results of the computational experiment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wolper, D.H.: The supervised learning no free lunch theorems. Technical report, NASA Ames Research Center, Moffett Field, California, USA (2001)
Kim, S.-W., Oommen, B.J.: A brief taxonomy and ranking of creative prototype reduction schemes. Pattern Analy. Appl. 6, 232–244 (2003)
Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithm. Mach. Learn. 33(3), 257–286 (2000)
Bhanu, B., Peng, J.: Adaptive integration image segmentation and object recognition. IEEE Trans. Syst. Man Cybern. 30(4), 427–441 (2000)
Czarnowski, I., Jędrzejowicz, P.: A new cluster-based instance selection algorithm. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, Robert J., Jain, Lakhmi C. (eds.) KES-AMSTA 2011. LNCS (LNAI), vol. 6682, pp. 436–445. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22000-5_45
Uno, T.: Multi-sorting algorithm for finding pairs of similar short substrings from large-scale string data. Knowl. Inf. Syst. 25, 229–251 (2009). https://doi.org/10.1007/s10115-009-0271-6
Sun, B., Chen, H., Wang, J., Xie, H.: Evolutionary under-sampling based bagging ensemble method for imbalanced data classification. Front. Comput. Sci. 12(2), 331–350 (2018)
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., Herrera, F.: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(4), 463–484 (2012)
Lin, W.-C., Chih-Fong, T., Hu, Y.-H., Jhang, J.-S.: Clustering-based undersampling in class-imbalanced data. Inf. Sci. 409, 17–26 (2017). https://doi.org/10.1016/j.ins.2017.05.008
Alcalá-Fdez, J., et al.: KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Multiple-Valued Logic Soft Comput. 17(2–3), 255–287 (2011). Accessed 10 Apr 2018
Czarnowski, I., Jędrzejowicz, P.: Cluster integration for the cluster-based instance selection. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ICCCI 2010. LNCS (LNAI), vol. 6421, pp. 353–362. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16693-8_37
Jędrzejowicz, P.: Social learning algorithm as a tool for solving some difficult scheduling problems. Found. Comput. Decis. Sci. 24, 51–66 (1999)
Hamo, Y., Markovitch, S.: The COMPSET algorithm for subset selection. In: Proceedings of the Nineteenth International Joint Conference for Artificial Intelligence, Edinburgh, Scotland, pp. 728–733 (2005)
Czarnowski, I.: Distributed learning with data reduction. In: Nguyen, N.T. (ed.) Transactions on Computational Collective Intelligence IV. LNCS (LNAI), vol. 6660, pp. 3–121. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21884-2_1
Talukdar, S., Baerentzen, L., Gove, A., de Souza, P.: Asynchronous teams: co-operation schemes for autonomous, computer-based agents. Technical report EDRC 18-59-96, Carnegie Mellon University, Pittsburgh (1996)
Czarnowski, I., Jędrzejowicz, P.: An approach to data reduction and integrated machine classification. New Gener. Comput. 28(1), 21–40 (2010)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, SanMateo (1993)
Fernandez, A., del Jesus, M.J., Herrera, F.: Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets. Int. J. Approximate Reasoning 50, 561–577 (2009). https://doi.org/10.1016/j.ijar.2008.11.004
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Czarnowski, I., Jędrzejowicz, P. (2018). Cluster-Based Instance Selection for the Imbalanced Data Classification. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11056. Springer, Cham. https://doi.org/10.1007/978-3-319-98446-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-98446-9_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98445-2
Online ISBN: 978-3-319-98446-9
eBook Packages: Computer ScienceComputer Science (R0)