Parallel random prism: a computationally efficient ensemble learner for classification

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Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, May, D. and Bramer, M. (2012) Parallel random prism: a computationally efficient ensemble learner for classification. In: Bramer, M. and Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX. Springer, London, pp. 21-34. ISBN 9781447147381 (Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence)

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To link to this item DOI: 10.1007/978-1-4471-4739-8_2

Abstract/Summary

Generally classifiers tend to overfit if there is noise in the training data or there are missing values. Ensemble learning methods are often used to improve a classifier's classification accuracy. Most ensemble learning approaches aim to improve the classification accuracy of decision trees. However, alternative classifiers to decision trees exist. The recently developed Random Prism ensemble learner for classification aims to improve an alternative classification rule induction approach, the Prism family of algorithms, which addresses some of the limitations of decision trees. However, Random Prism suffers like any ensemble learner from a high computational overhead due to replication of the data and the induction of multiple base classifiers. Hence even modest sized datasets may impose a computational challenge to ensemble learners such as Random Prism. Parallelism is often used to scale up algorithms to deal with large datasets. This paper investigates parallelisation for Random Prism, implements a prototype and evaluates it empirically using a Hadoop computing cluster.

Item Type:	Book or Report Section
Refereed:	Yes
Divisions:	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:	30168
Uncontrolled Keywords:	AI, Artificial Intelligence, Data Mining and Knowledge Discovery
Publisher:	Springer

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Parallel random prism: a computationally efficient ensemble learner for classification

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