Abstract
Data distribution plays a key role in the performance of various classification algorithms. Artificial neural network (ANN) has been widely applied in considerable complex tasks because of its excellent universal approximation capability. Although the floating centroids method (FCM) provides an effective and diverse output encoding that removes the fixed centroids constraint, the adaptive mechanism between the FCM-based neural network classifier and the data distribution has not been studied. In this paper, we design an adversarial network to investigate the characteristics of FCM and adopt a particle swarm optimization to evolve the data distribution. Experimental results demonstrated that FCM show the characteristics of diversified centroids, flexible clustering, and insensitivity to data scale, thereby obtaining competitive performance.
Keywords
- Artificial neural network
- Classification
- Data distribution
- Floating centroids
- Fixed centroids
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References
Bao, F., Deng, Y., Kong, Y., Ren, Z., Suo, J., Dai, Q.: Learning deep landmarks for imbalanced classification. IEEE Trans. Neural Netw. Learn. Syst. 31(8), 2691–2704 (2020)
Chen, C.L.P., Liu, Z.: Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans. Neural Netw. Learn. Syst. 29(1), 10–24 (2018)
Ning, O., Zhu, T., Lin, L.: Convolutional neural network trained by joint loss for hyperspectral image classification. IEEE Geoence Remote Sens. Lett. 16(3), 457–461 (2019)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105, January 2012
Wang, L., Yang, B., Chen, Y., Zhang, X., Orchard, J.: Improving neural-network classifiers using nearest neighbor partitioning. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2255–2267 (2017)
Marek, M., Struski, U., Figueiredo, M.: A classification-based approach to semi-supervised clustering with pairwise constraints. Neural Netw. 127, 193–203 (2020)
Mousavi, S., Zhu, W., Ellsworth, W., Beroza, G.: Unsupervised clustering of seismic signals using deep convolutional autoencoders. IEEE Geoence Remote Sens. Lett. 16(11), 1693–1697 (2019)
Erturul, M.F.: A novel clustering method built on random weight artificial neural networks and differential evolution. Soft. Comput. 24(16), 12067–12078 (2020)
Shao, Z., Zhou, W., Deng, X., Zhang, M., Cheng, Q.: Multilabel remote sensing image retrieval based on fully convolutional network. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 13(1), 318–328 (2020)
Li, P., Han, L., Tao, X., Zhang, X., Grecos, C., Plaza, A.: Hashing nets for hashing: a quantized deep learning to hash framework for remote sensing image retrieval. IEEE Trans. Geoence Remote Sens. 58(10), 7331–7345 (2020)
Wang, L., Yang, B., Chen, Y., Abraham, A., Sun, H., Chen, Z., Wang, H.: Improvement of neural network classifier using floating centroids. Knowl. Inf. Syst. 31(3), 433–454 (2012)
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1735–1742, June 2006
Liu, W., Wen, Y., Yu, Z., Li, M., Raj B., Song, L.: SphereFace: deep hypersphere embedding for face recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 6738–6746, June 2017
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770–778, June 2016
Lin, M., Cheng, Q., Yan, S.: Network in network. In Proceedings of International Conference on Learning Representations, April 2014
Wang, L., Orchard, J.: Investigating the evolution of a neuroplasticity network for learning. IEEE Trans. Syst. Man Cybern. Syst. 49(10), 2131–2143 (2019)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Quentin, M., Fabrice, P., Desideri, J.A.: Stochastic multiple gradient descent algorithm. Eur. J. Oper. Res. 271(3), 808–817 (2018)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: International Symposium on Micro Machine and Human Science, pp. 39–43, October 1995
Lin, C.T., Prasad, M., Saxena, A.: An improved polynomial neural network classifier using real-coded genetic algorithm. IEEE Trans. Syst. Man Cybern. Syst. 45(11), 1389–1401 (2015)
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, No. 61903156. Shandong Provincial Natural Science Foundation No. ZR2019MF040, No. ZR2018LF005. Shandong Provincial Key R&D Program under Grant No. 2019GGX101041, No. 2018CXGC0706, No. 2017CXZC1206. Taishan Scholars Program of Shandong Province, China, under Grant No. tsqn201812077.
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Zhang, X., Fan, J., Wang, L., Yang, B. (2021). Investigating Data Distribution for Classification Using PSO with Adversarial Network. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_26
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DOI: https://doi.org/10.1007/978-3-030-71187-0_26
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