Abstract
In class-specific cost regulation extreme learning machine (CCR-ELM) for the class imbalance problems, the key parameters, including the number of hidden nodes, the input weights, the hidden biases and the tradeoff factors are normally chosen randomly or preset by human. This made the algorithm responding slowly and generalization worse. Unsuitable quantity of hidden nodes might form some useless neuron nodes and make the network complex. So an improved CCR-ELM based on particle swarm optimization with variable length is present. Each particle consists of above key parameters and its length varies with the number of hidden nodes. The experimental results for nine imbalance dataset show that particle swarm optimization with variable length can find better parameters of CCR-ELM and corresponding CCR-ELM had better classification accuracy. In addition, the classification performance of the proposed classification algorithm is relatively stable under different imbalance ratios.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chawla, N.V., Bowyer, K.W., Hall, L.O., et al.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16(1), 321–357 (2002)
Ramentol, E., Caballero, Y., Bello, R., et al.: SMOTE-RS B: A hybrid preprocessing approach based on oversampling and undersampling for high imbalanced datasets using SMOTE and rough sets theory. Knowl. Inf. Syst. 33(2), 245–265 (2012)
Gao, M., Hong, X., Chen, S., et al.: A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems. Neurocomputing 74(17), 3456–3466 (2011)
Sun, Y., Kamel, M.S., Wong, A.K.C., et al.: Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn. 40(12), 3358–3378 (2007)
Wu, G., Chang, E.Y.: KBA: kernel boundary alignment considering imbalanced data distribution. IEEE Trans. Knowl. Data Eng. 17(6), 786–795 (2005)
Zong, W., Huang, G.B., Chen, Y.: Weighted extreme learning machine for imbalance learning. Neurocomputing 101, 229–242 (2013)
Xiao, W., Zhang, J., Li, Y., et al.: Class-specific cost regulation extreme learning machine for imbalanced classification. Neurocomputing 261, 70–82 (2017)
Rong, H.J., Huang, G.B., Sundararajan, N., et al.: Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans. Syst. Man Cybern. Part B 39(4), 1067–1072 (2009)
Mirza, B., Lin, Z., Liu, N.: Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift. Neurocomputing 149, 316–329 (2015)
Huang, G.B., Chen, L., Siew, C.K.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Netw. 17(4), 879–892 (2006)
Huang, G., Song, S., Gupta, J.N., et al.: Semi-supervised and unsupervised extreme learning machines. IEEE Trans. Cybern. 44(12), 2405–2417 (2014)
Huang, D.S., Ip, H.H., Law, K.C., et al.: Zeroing polynomials using modified constrained neural network approach. IEEE Trans. Neural Netw. 16(3), 721–732 (2005)
Ertam, F., Avcı, E.: A new approach for internet traffic classification: GA-WK-ELM. Measurement 95, 135–142 (2016)
Guo, Y.N., Zhang, P., Cheng, J., et al.: Interval multi-objective quantum-inspired cultural algorithms. Neural Comput. Appl. 1–14 (2016)
Han, F., Yao, H.F., Ling, Q.H.: An improved evolutionary extreme learning machine based on particle swarm optimization. Neurocomputing 116, 87–93 (2013)
Guo, Y.-n., Zhang, P., Cheng, J., Zhang, Y., Yang, L., Shen, X., Fang, W.: An improved weighted ELM with Krill Herd algorithm for imbalanced learning. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds.) ICSI 2017. LNCS, vol. 10386, pp. 371–378. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61833-3_39
Hu, C.Y., Hu, B.J., Xiong, Y.H.: Mobile agent routing using variable-dimension PSO algorithm based on chord-length parameterization. National Doctoral Academic Forum on Information and Communications Technology, IET, 7–7 (2013)
Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences (2010). http://archive.ics.uci.edu/mlS
Yu, H., Sun, C., Yang, X., et al.: ODOC-ELM: Optimal decision outputs compensation-based extreme learning machine for classifying imbalanced data. Knowl.-Based Syst. 92, 55–70 (2016)
Acknowledgements
This work is supported by National Natural Science Foundation of China under Grant 61573361, National Key Research and Development Program under Grant 2016YFC0801406, and Six talent peaks project in Jiangsu Province under Grant No.2017-DZXX-046.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Guo, Yn., Zhang, P., Cui, N., Chen, J., Cheng, J. (2018). VPSO-Based CCR-ELM for Imbalanced Classification. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-93818-9_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93817-2
Online ISBN: 978-3-319-93818-9
eBook Packages: Computer ScienceComputer Science (R0)