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Producing Implicit Diversity in ANN Ensembles
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
2012 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Combining several ANNs into ensembles normally results in a very accurate and robust predictive models. Many ANN ensemble techniques are, however, quite complicated and often explicitly optimize some diversity metric. Unfortunately, the lack of solid validation of the explicit algorithms, at least for classification, makes the use of diversity measures as part of an optimization function questionable. The merits of implicit methods, most notably bagging, are on the other hand experimentally established and well-known. This paper evaluates a number of straightforward techniques for introducing implicit diversity in ANN ensembles, including a novel technique producing diversity by using ANNs with different and slightly randomized link structures. The experimental results, comparing altogether 54 setups and two different ensemble sizes on 30 UCI data sets, show that all methods succeeded in producing implicit diversity, but that the effect on ensemble accuracy varied. Still, most setups evaluated did result in more accurate ensembles, compared to the baseline setup, especially for the larger ensemble size. As a matter of fact, several setups even obtained significantly higher ensemble accuracy than bagging. The analysis also identified that diversity was, relatively speaking, more important for the larger ensembles. Looking specifically at the methods used to increase the implicit diversity, setups using the technique that utilizes the randomized link structures generally produced the most accurate ensembles.

Place, publisher, year, edition, pages
IEEE , 2012.
Keyword [en]
Artificial neural networks, implicit diversity, ANN ensemble technique, Computer Science, Computer Science, Ensemble Learning, Neural Networks
National Category
Software Engineering
Research subject
Bussiness and IT
Identifiers
URN: urn:nbn:se:hb:diva-6809DOI: 10.1109/IJCNN.2012.6252713ISI: 000309341302072Local ID: 2320/11277ISBN: 978-1-4673-1488-6 (print)ISBN: 978-1-4673-1489-3 (print)OAI: oai:DiVA.org:hb-6809DiVA: diva2:887512
Conference
Neural Networks (IJCNN), The 2012 International Joint Conference on
Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2017-05-02

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