Abstract
Floating centroid method (FCM) offers an efficient way to solve a fixed-centroid problem for the neural network classifiers. However, evolutionary computation as its optimization method restrains the FCM to achieve satisfactory performance for different neural network structures, because of the high computational complexity and inefficiency. Traditional gradient-based methods have been extensively adopted to optimize the neural network classifiers. In this study, a gradient-based floating centroid (GDFC) method is introduced to address the fixed centroid problem for the neural network classifiers optimized by gradient-based methods. Furthermore, a new loss function for optimizing GDFC is introduced. The experimental results display that GDFC obtains promising classification performance than the comparison methods on the benchmark datasets.
M. Islam and S. Liu—Both authors contribute equally to this article.
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Acknowledgements
This work was supported by National Natural Science Foundation of China under Grant No. 61872419, No. 61573166, No. 61572230, No. 61873324, No. 81671785, No. 61672262. Shandong Provincial Natural Science Foundation No. ZR2019MF040, No. ZR2018LF005. Shandong Provincial Key R&D Program under Grant No. 2019GGX101041, No. 2018GGX101048, No. 2016ZDJS01A12, No. 2016GGX101001, No. 2017CXZC1206. Taishan Scholar Project of Shandong Province, China, under Grant No. tsqn201812077.
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Islam, M., Liu, S., Zhang, X., Wang, L. (2019). Improving Neural Network Classifier Using Gradient-Based Floating Centroid Method. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_45
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