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.
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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
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DOI: https://doi.org/10.1007/978-3-642-22910-7_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22909-1
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