Abstract
This paper is devoted to data decomposition analysis. We decompose data into functional groups depending on their complexity in terms of classification. We use consensus of classifiers as an effective algorithm for data decomposition. Present research considers data decomposition into two subsets of “easy” and “difficult” or “ambiguous” data. The easiest part of data is classified during decomposition using consensus of classifiers. For other part of data one has to apply other classifiers or classifier combination. One can prove experimentally that afore mentioned data decomposition using optimal consensus of classifiers leads to better performance and generalization ability of the entire classification algorithm.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Frank, A., Asuncion, A.: UCI repository of machine learning databases. Technical report. University of California, School of Information and Computer Sciences Irvine, CA (2010)
Freund, Y., Schapire, R.E.: A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995). doi:10.1007/3-540-59119-2_166
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)
Kuncheva, L.: Combining Pattern Classifiers. Wiley, Hoboken (2014)
Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT press, Cambridge (2012)
Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: a new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1619–1630 (2006)
Rokach, L.: Pattern Classification Using Ensemble Methods. World Scientific, Hackensack (2009)
Vorontsov, K.V.: Splitting and similarity phenomena in the sets of classifiers and their effect on the probability of overfitting. Pattern Recogn. Image Anal. 19(3), 412–420 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Tayanov, V., Krzyżak, A., Suen, C. (2017). Classification Boosting by Data Decomposition Using Consensus-Based Combination of Classifiers. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-59876-5_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59875-8
Online ISBN: 978-3-319-59876-5
eBook Packages: Computer ScienceComputer Science (R0)