Abstract
Divide-and-conquer approach has been recognized in multiple classifier systems aiming to utilize local expertise of individual classifiers. In this study we experimentally investigate three strategies for building local classifiers that are based on different routines of sampling data for training. The first two strategies are based on clustering the training data and building an individual classifier for each cluster or a combination. The third strategy divides the training set based on a selected feature and trains a separate classifier for each subset. Experiments are carried out on simulated and real datasets. We report improvement in the final classification accuracy as a result of combining the three strategies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Breiman, L.: Bagging predictors. Machine Learning 24(1996), 123–140 (1996)
Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity creation methods: a survey and categorization. Inf. Fusion 6, 5–20 (2005)
Frosyniotis, D., Stafylopatis, A., Likas, A.: A divide-and-conquer method for multi-net classifiers. Pattern Analysis and Appl. 6, 32–40 (2003)
Harries, M.: Splice-2 comparative evaluation: Electricity pricing. Technical Report UNSW-CSE -TR-9905, Artif. Intell. Group, School of Computer Science and Engineering, The University of New South Wales, Sidney (1999)
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: data mining, inference and prediction. Springer, Heidelberg (2005)
Jacobs, R., Jordan, M., Nowlan, S., Hinton, G.: Adaptive mixtures of local experts. Neural Computation 3, 79–87 (1991)
Katakis, I., Tsoumakas, G., Vlahavas, I.: Tracking recurring contexts using ensemble classifiers: an application to email filtering. Knowledge and Inf. Syst. 22, 371–391 (2009)
King, G., Zeng, L.: Logistic regression in rare events data. Political Analysis 9(2001), 137–163 (2001)
Kuncheva, L.: Clustering-and-selection model for classifier combination. In: Proc. the 4th Int. Conf. Knowledge-Based Intell. Eng. Syst. and Allied Technologies, Brighton, UK, pp. 185–188 (2000)
Kuncheva, L.I., Rodriguez, J.J.: Classifier ensembles with a random linear oracle. IEEE Trans. Knowledge and Data Eng. 19, 500–508 (2007)
Lim, M., Sohn, S.: Cluster-based dynamic scoring model. Expert Systems with Appl. 32, 427–431 (2007)
Liu, R., Yuan, B.: Multiple classifiers combination by clustering and selection. Inf. Fusion 2, 163–168 (2001)
Newman, D.J., Asuncion, A.: UCI machine learning repository, http://archive.ics.uci.edu/ml/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Žliobaitė, I. (2011). Three Data Partitioning Strategies for Building Local Classifiers. In: Okun, O., Valentini, G., Re, M. (eds) Ensembles in Machine Learning Applications. Studies in Computational Intelligence, vol 373. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22910-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-22910-7_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22909-1
Online ISBN: 978-3-642-22910-7
eBook Packages: EngineeringEngineering (R0)