NDLI: On-line bagging Negative Correlation Learning

Content Provider	IEEE Xplore Digital Library
Author	Minku, F.L. Xin Yao
Copyright Year	2008
Description	Author affiliation: Centre of Excellence for Res. in Comput. Intell. & Applic. (CERCIA), Univ. of Birmingham, Birmingham (Minku, F.L.; Xin Yao)
Abstract	Negative Correlation Learning (NCL) has been showing to outperform other ensemble learning approaches in off-line mode. A key point to the success of NCL is that the learning of an ensemble member is influenced by the learning of the others, directly encouraging diversity. However, when applied to on-line learning, NCL presents the problem that part of the diversity has to be built a priori, as the same sequence of training data is sent to all the ensemble members. In this way, the choice of the base models to be used is limited and the use of more adequate neural network models for the problem to be solved may be not possible. This paper proposes a new method to perform on-line learning based on NCL and On-line Bagging. The method directly encourages diversity, as NCL, but sends a different sequence of training data to each one of the base models in an on-line bagging way. So, it allows the use of deterministic base models such as Evolving Fuzzy Neural Networks (EFuNNs), which are specifically designed to perform on-line learning. Experiments show that on-line bagging NCL using EFuNNs have better accuracy than NCL applied to online learning using on-line Multi-Layer Perceptrons (MLPs) in 4 out of 5 classification databases. Besides, on-line bagging NCL using EFuNNs manage to attain similar accuracy to NCL using off-line MLPs.
Starting Page	1374
Ending Page	1381
File Size	159713
Page Count	8
File Format	PDF
ISBN	9781424418206
ISSN	10987576
DOI	10.1109/IJCNN.2008.4633977
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bagging Training Artificial neural networks Boosting Learning systems Databases Classification algorithms
Content Type	Text
Resource Type	Article

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