NDLI: Hybrid ensembles and coincident-failure diversity

Content Provider	IEEE Xplore Digital Library
Author	Wenjia Wang Partridge, D. Etherington, J.
Copyright Year	2001
Description	Author affiliation: Dept. of Comput., Bradford Univ., UK (Wenjia Wang)
Abstract	This paper presents an approach for constructing hybrid ensembles with two categories of members: trained neural networks and decision trees in the hope of increasing diversity and reducing coincident failures. The diversity among the members of an ensemble has been generally recognised as a key factor for improving the overall performance of the ensemble. Of the two heterogeneous members evaluated with a number of the diversity measures, we found that there is a statistically low level coincident-failure diversity among homogeneous members but a relatively high level diversity between heterogeneous members. This provides theoretical basis for constructing hybrid ensembles with members developed by different methodologies. The hybrid ensembles have been built for a real-world problem: predicting training injury of military recruits. Their performances are evaluated in terms of diversity, reliability and prediction accuracy, and also compared with the homogeneous ensembles of neural nets or decision trees. The results indicate that the hybrid ensembles have a considerably high level diversity and thus are able to produce a better performance.
Sponsorship	Int. Neural Network Soc.
Starting Page	2376
Ending Page	2381
File Size	580052
Page Count	6
File Format	PDF
ISBN	0780370449
ISSN	10987576
DOI	10.1109/IJCNN.2001.938738
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Diversity reception Decision trees Injuries Recruitment Bagging Boosting Computer networks Computer science Biomedical engineering
Content Type	Text
Resource Type	Article

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