ISNN 2016: Advances in Neural Networks – ISNN 2016 pp 474-481 | Cite as
A Novel Incremental Class Learning Technique for Multi-class Classification
Abstract
In this paper, a novel technique for multi-class classification, which is independent of the number of class constraints and can learn the new classes it encounters, is developed. The developed technique enables remodelling of the network to adapt to the dynamic nature of non-stationary input samples. It not only can learn the new classes, but also the new patterns created in the input. The proposed algorithm is evaluated using various benchmark datasets and a comparative study of classification performance shows that the proposed algorithm is superior.
Keywords
ELM OS-ELM Incremental class learning Sequential learning Multi-class classificationNotes
Acknowledgement
The authors would like to acknowledge the funding support from the National Natural Science Foundation of P. R. China (under Grants 51009017 and 51379002), Applied Basic Research Funds from Ministry of Transport of P. R. China (under Grant 2012-329-225-060), and Pro-gram for Liaoning Excellent Talents in University (under Grant LJQ2013055).
References
- 1.Liang, N.-Y., Huang, G.-B., Saratchandran, P., Sundararajan, N.: A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Netw. 17, 1411–1423 (2006)CrossRefGoogle Scholar
- 2.Wang, N., Er, M.J., Han, M.: Parsimonious extreme learning machine using recursive orthogonal least squares. IEEE Trans. Neural Netw. Learn. Syst. 25, 1828–1841 (2014)CrossRefGoogle Scholar
- 3.Wang, N., Er, M.J., Han, M.: Generalized single-hidden layer feedforward networks for regression problems. IEEE Trans. Neural Netw. Learn. Syst. 26, 1161–1176 (2015)MathSciNetCrossRefGoogle Scholar
- 4.Zhao, Z., Chen, Z., Chen, Y., Wang, S., Wang, H.: A class incremental extreme learning machine for activity recognition. Cogn. Comput. 6, 423–431 (2014)CrossRefGoogle Scholar
- 5.Zhang, B., Su, J., Xu, X.: A class-incremental learning method for multi-class support vector machines in text classification. In: 2006 International Conference on Machine Learning and Cybernetics, pp. 2581–2585. IEEE (2006)Google Scholar
- 6.Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)CrossRefGoogle Scholar
- 7.Li, M.-B., Huang, G.-B., Saratchandran, P., Sundararajan, N.: Fully complex extreme learning machine. Neurocomputing 68, 306–314 (2005)CrossRefGoogle Scholar
- 8.Zhu, Q.-Y., Qin, A.K., Suganthan, P.N., Huang, G.-B.: Evolutionary extreme learning machine. Pattern Recogn. 38, 1759–1763 (2005)CrossRefMATHGoogle Scholar
- 9.Huang, G.-B., Chen, L., Siew, C.-K.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Netw. 17, 879–892 (2006)CrossRefGoogle Scholar
- 10.LI, B., Wang, J., Li, Y., Song, Y.: An improved on-line sequential learning algorithm for extreme learning machine. In: Liu, D., Fei, S., Hou, Z.-G., Zhang, H., Sun, C. (eds.) ISNN 2007, Part I. LNCS, vol. 4491, pp. 1087–1093. Springer, Heidelberg (2007)CrossRefGoogle Scholar
- 11.Rong, H.-J., Huang, G.-B., Sundararajan, N., Saratchandran, P.: Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans. Syst. Man Cybern. B Cybern. 39, 1067–1072 (2009)CrossRefGoogle Scholar
- 12.Huang, G.-B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern. 2, 107–122 (2011)CrossRefGoogle Scholar
- 13.Wang, X., Han, M.: Improved extreme learning machine for multivariate time series online sequential prediction. Eng. Appl. Artif. Intell. 40, 28–36 (2015)CrossRefGoogle Scholar