A Review on Diverse Ensemble Methods for Classification
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2013, Vol 1, Issue 4
Abstract
Ensemble methods for different classifiers like Bagging and Boosting which combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity ofthe members of an ensemble is known to be an important factor in determining its generalization error. DECORATE (Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples), that directly constructs diverse hypotheses using additional artificially-constructed training examples. The technique is a simple, general meta-learner that can use any strong learner as a base classifier to build diverse committees. The diverse ensembles produced by DECORATE are very effective for reducing the amount of supervision required for building accurate models. DECORATE ensembles can also be used to reduce supervision through active learning, in which the learner selects the most informative examples from a pool of unlabeled examples, such that acquiring their labels will increase the accuracy of the classifier.
Authors and Affiliations
Prachi S. Adhvaryu
Keywords
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