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2016 | OriginalPaper | Chapter

Choice of Best Samples for Building Ensembles in Dynamic Environments

Authors : Joana Costa, Catarina Silva, Mário Antunes, Bernardete Ribeiro

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

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Abstract

Machine learning approaches often focus on optimizing the algorithm rather than assuring that the source data is as rich as possible. However, when it is possible to enhance the input examples to construct models, one should consider it thoroughly. In this work, we propose a technique to define the best set of training examples using dynamic ensembles in text classification scenarios. In dynamic environments, where new data is constantly appearing, old data is usually disregarded, but sometimes some of those disregarded examples may carry substantial information. We propose a method that determines the most relevant examples by analysing their behaviour when defining separating planes or thresholds between classes. Those examples, deemed better than others, are kept for a longer time-window than the rest. Results on a Twitter scenario show that keeping those examples enhances the final classification performance.

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previous chapter 2D Recurrent Neural Networks for Robust Visual Tracking of Non-Rigid Bodies
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Metadata
Title
Choice of Best Samples for Building Ensembles in Dynamic Environments
Authors
Joana Costa
Catarina Silva
Mário Antunes
Bernardete Ribeiro
Copyright Year
2016
Book
Engineering Applications of Neural Networks

Print ISBN: 978-3-319-44187-0

Electronic ISBN: 978-3-319-44188-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-44188-7_3

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