Towards an Improved Ensemble Learning Model of Artificial Neural Networks-Reference-Cited by-同舟云学术

Towards an Improved Ensemble Learning Model of Artificial Neural Networks

Published:2014 Issue: Volume: Page:76-106
ISSN:2327-1981
Container-title:Advances in Data Mining and Database Management
language:
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Author:

Anifowose Fatai¹,Labadin Jane¹,Abdulraheem Abdulazeez²

Affiliation:

1. Universiti Malaysia Sarawak, Malaysia

2. King Fahd University of Petroleum and Minerals, Saudi Arabia

Abstract

Artificial Neural Networks (ANN) have been widely applied in petroleum reservoir characterization. Despite their wide use, they are very unstable in terms of performance. Ensemble machine learning is capable of improving the performance of such unstable techniques. One of the challenges of using ANN is choosing the appropriate number of hidden neurons. Previous studies have proposed ANN ensemble models with a maximum of 50 hidden neurons in the search space thereby leaving rooms for further improvement. This chapter presents extended versions of those studies with increased search spaces using a linear search and randomized assignment of the number of hidden neurons. Using standard model evaluation criteria and novel ensemble combination rules, the results of this study suggest that having a large number of “unbiased” randomized guesses of the number of hidden neurons beyond 50 performs better than very few occurrences of those that were optimally determined.

Publisher

IGI Global

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