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A constructive algorithm for unsupervised learning with incremental neural network
ARTICLE

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Journal of Applied Research and Technology Volume 13, Number 2, ISSN 1665-6423 Publisher: Elsevier Ltd

Abstract

Artificial neural network (ANN) has wide applications such as data processing and classification. However, comparing with other classification methods, ANN needs enormous memory space and training time to build the model. This makes ANN infeasible in practical applications. In this paper, we try to integrate the ideas of human learning mechanism with the existing models of ANN. We propose an incremental neural network construction framework for unsupervised learning. In this framework, a neural network is incrementally constructed by the corresponding subnets with individual instances. First, a subnet maps the relation between inputs and outputs for an observed instance. Then, when combining multiple subnets, the neural network keeps the corresponding abilities to generate the same outputs with the same inputs. This makes the learning process unsupervised and inherent in this framework.In our experiment, Reuters-21578 was used as the dataset to show the effectiveness of the proposed method on text classification. The experimental results showed that our method can effectively classify texts with the best F1-measure of 92.5%. It also showed the learning algorithm can enhance the accuracy effectively and efficiently. This framework also validates scalability in terms of the network size, in which the training and testing times both showed a constant trend. This also validates the feasibility of the method for practical uses.

Citation

Wang, J.H., Wang, H.Y., Chen, Y.L. & Liu, C.M. (2015). A constructive algorithm for unsupervised learning with incremental neural network. Journal of Applied Research and Technology, 13(2), 188-196. Elsevier Ltd. Retrieved January 23, 2020 from .

This record was imported from Journal of Applied Research and Technology on January 29, 2019. Journal of Applied Research and Technology is a publication of Elsevier.

Full text is availabe on Science Direct: http://dx.doi.org/10.1016/j.jart.2015.06.017

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