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On Extending Neural Networks with Loss Ensembles for Text Classification
lib:be4e8f98273759ba (v1.0.0)
Authors: Hamideh Hajiabadi,Diego Molla-Aliod,Reza Monsefi
Where published: ALTA 2017 12
ArXiv: 1711.05170
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1711.05170v1
Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta learning framework, ensemble techniques can easily be applied to many machine learning techniques. In this paper we propose a neural network extended with an ensemble loss function for text classification. The weight of each weak loss function is tuned within the training phase through the gradient propagation optimization method of the neural network. The approach is evaluated on several text classification datasets. We also evaluate its performance in various environments with several degrees of label noise. Experimental results indicate an improvement of the results and strong resilience against label noise in comparison with other methods.
Where published: ALTA 2017 12
ArXiv: 1711.05170
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1711.05170v1
Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta learning framework, ensemble techniques can easily be applied to many machine learning techniques. In this paper we propose a neural network extended with an ensemble loss function for text classification. The weight of each weak loss function is tuned within the training phase through the gradient propagation optimization method of the neural network. The approach is evaluated on several text classification datasets. We also evaluate its performance in various environments with several degrees of label noise. Experimental results indicate an improvement of the results and strong resilience against label noise in comparison with other methods.
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