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Random Brains
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we introduce and evaluate a novel method, called random brains, for producing neural network ensembles. The suggested method, which is heavily inspired by the random forest technique, produces diversity implicitly by using bootstrap training and randomized architectures. More specifically, for each base classifier multilayer perceptron, a number of randomly selected links between the input layer and the hidden layer are removed prior to training, thus resulting in potentially weaker but more diverse base classifiers. The experimental results on 20 UCI data sets show that random brains obtained significantly higher accuracy and AUC, compared to standard bagging of similar neural networks not utilizing randomized architectures. The analysis shows that the main reason for the increased ensemble performance is the ability to produce effective diversity, as indicated by the increase in the difficulty diversity measure.

Place, publisher, year, edition, pages
IEEE , 2013.
Keyword [en]
Data mining, Machine Learning
National Category
Computer Science Computer and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-7057Local ID: 2320/12922OAI: oai:DiVA.org:hb-7057DiVA: diva2:887764
Conference
International Joint Conference on Neural Networks, Dallas, TX, USA, August 4-9, 2013.
Note

Sponsorship:

Swedish Foundation for Strategic

Research through the project High-Performance Data Mining for Drug Effect

Detection (IIS11-0053) and the Knowledge Foundation through the project

Big Data Analytics by Online Ensemble Learning (20120192)

Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2017-05-02

Open Access in DiVA

fulltext(144 kB)141 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
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