Abstract
In this chapter, we present ETL committee, an ensemble method that uses ETL as a base learner. The ETL committee strategy relies on the use of training data manipulation to create an ensemble of ETL classifiers. ETL committee combines the main ideas of bagging and random subspaces. From bagging, we borrow the bootstrap sampling method. From random subspaces, we use the feature sampling idea. In the ETL committee training, we use ETL with template sampling, which provides an additional randomization step. As far as we know, this is the first study that uses transformation rule learning as the base learner for an ensemble method. This chapter is organized as follows. In Sect. 3.1, we explain the main idea behind ensemble methods. In Sect. 3.2, we detail the ETL committee training phase. In Sect. 3.3, we detail the classification phase. Finally, in Sect. 3.4, we present some related works.
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dos Santos, C.N., Milidiú, R.L. (2012). ETL Committee. In: Entropy Guided Transformation Learning: Algorithms and Applications. SpringerBriefs in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-2978-3_3
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DOI: https://doi.org/10.1007/978-1-4471-2978-3_3
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