A novel online ensemble convolutional neural networks for streaming data
Author(s)
Pham, XC
Nguyen, TTT
Liew, AWC
Griffith University Author(s)
Year published
2019
Metadata
Show full item recordAbstract
In this study, we introduce an online ensemble method based on convolutional neural networks (CNNs) for streaming data. Recent work has shown that a convolution operation has been an effective way to extract features. In particular, we proposed a CNN working in an online manner as a base classifier. Then, an ensemble approach is devised to boost the performance of all base classifiers. We also propose two loss terms which can adapt to the imbalanced data stream as well as handling the forgetting issue of deep networks. The experiments conducted on a number of datasets chosen from different sources demonstrate that the proposed ...
View more >In this study, we introduce an online ensemble method based on convolutional neural networks (CNNs) for streaming data. Recent work has shown that a convolution operation has been an effective way to extract features. In particular, we proposed a CNN working in an online manner as a base classifier. Then, an ensemble approach is devised to boost the performance of all base classifiers. We also propose two loss terms which can adapt to the imbalanced data stream as well as handling the forgetting issue of deep networks. The experiments conducted on a number of datasets chosen from different sources demonstrate that the proposed ensemble approach performs significantly better than a single network and some well-known online learning algorithms including additive models and Online Bagging.
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View more >In this study, we introduce an online ensemble method based on convolutional neural networks (CNNs) for streaming data. Recent work has shown that a convolution operation has been an effective way to extract features. In particular, we proposed a CNN working in an online manner as a base classifier. Then, an ensemble approach is devised to boost the performance of all base classifiers. We also propose two loss terms which can adapt to the imbalanced data stream as well as handling the forgetting issue of deep networks. The experiments conducted on a number of datasets chosen from different sources demonstrate that the proposed ensemble approach performs significantly better than a single network and some well-known online learning algorithms including additive models and Online Bagging.
View less >
Conference Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
11953
Subject
Artificial intelligence