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2018 | OriginalPaper | Chapter

An Adaptive Cluster-Based Ensemble Learner for Computational Biology

Authors : Niti Jain, Ambar Maini

Published in: Advanced Computational and Communication Paradigms

Publisher: Springer Singapore

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Abstract

In quantitative biology, discovering a class when presented with a large bimolecular dataset poses a big problem. However, ensemble learning approach has been helpful in various complex areas of decision-making. So, in this paper, we propose a cluster-based ensemble learner called adaptive cluster-based ensemble learner (ACEL) which incorporates the prior knowledge of the datasets into the cluster ensemble framework. ACEL computes the cluster boundaries using three diverse clustering algorithms to obtain clusters for classification decision. ACEL learns by transforming the obtained clusters into rules and performing adaptive rule tuning to optimize the classification decision. The cluster-based classification results are then processed using majority voting algorithm. The proposed approach is compared with other supervised benchmark algorithms using seven problems from the field of biology. The experiments performed on benchmark datasets show that ACEL works effectively in classifying datasets.

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Alves, P., Liu, S., Wang, D., Gerstein, M.: Multiple-Swarm ensembles: improving the predictive power and robustness of predictive models and its use in computational biology. IEEE/ACM Trans. Comput. Biol. Bioinform. 5963, 1–1 (2017). https://doi.org/10.1109/TCBB.2017.2691329

Dudoit, S., Fridlyand, J.: Bagging to improve the accuracy of a clustering procedure. Bioinformatics 19, 1090–1099 (2003). https://doi.org/10.1093/bioinformatics/btg038

Giotis, I., Petkov, N.: Cluster-based adaptive metric classification. J. Neurocomputing 91, 33–40 (2012). https://doi.org/10.1016/j.neucom.2011.10.018

Jain, A.: Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31, 651–666 (2010). https://doi.org/10.1016/j.patrec.2009.09.011

Kittler, J.: Combining classifiers: a theoretical framework. Pattern Anal. Appl. 1, 18–27 (1998). https://doi.org/10.1007/BF01238023

Kuncheva, L., Ll, G., Kingdom, U., Duin, R.: Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognit. 34(2), 299–314 (2001). https://doi.org/10.1016/S0031-3203(99)00223-X

Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006). https://doi.org/10.1109/MCAS.2006.1688199

Quinlan, J.: Bagging, boosting, and C4.5. Proc. Thirteen. Natl. Conf. Artif. Intell. 5, 725–730 (1996). https://doi.org/10.1023/A:1018054314350

Rodrguez, J., Maudes, J.: Boosting recombined weak classifiers. Pattern Recogn. Lett. 29, 1049–1059 (2008). https://doi.org/10.1016/j.patrec.2007.06.019

10.

Skurichina, M., Duin, R.: Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal. Appl. 5, 121–135 (2002). https://doi.org/10.1007/s100440200011

11.

Teng, G., He, C., Xiao, J., He, J., Zhu, B., Jiang, X.: Cluster ensemble framework based on the group method of data handling. Appl. Soft Comput. 43, 35–46 (2016). https://doi.org/10.1016/j.asoc.2016.01.043

12.

UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/

13.

Xu, L., Krzyzak, A., Suen, C.: A method of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. Syst. Man Cybern. 22, 418–435 (1992). https://doi.org/10.1109/34.368145

14.

Yu, Z., Chen, H., You, J., Wong, H., Liu, J., Li, L., Han, G.: Double selection based semi-supervised clustering ensemble for tumor clustering from gene expression profiles. IEEE/ACM Trans. Comput. Biol. Bioinforma. 11, 727–740 (2014). https://doi.org/10.1109/TCBB.2014.2315996

15.

Yu, Z., Member, S., Luo, P., You, J., Wong, H., Leung, H., Wu, S.: Incremental semi-supervised clustering ensemble for high dimensional data clustering. Tkde 28, 701–714 (2016). https://doi.org/10.1109/TKDE.2015.2499200

Title: An Adaptive Cluster-Based Ensemble Learner for Computational Biology
Authors: Niti Jain
Ambar Maini
Publisher: Springer Singapore
Book: Advanced Computational and Communication Paradigms
Print ISBN: 978-981-10-8236-8

Electronic ISBN: 978-981-10-8237-5

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-981-10-8237-5_56

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