VPSO-Based CCR-ELM for Imbalanced Classification
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.
KeywordsVariable length Particle swarm optimization Class-specific cost regulation extreme learning machine The class imbalance
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.
- 14.Guo, Y.N., Zhang, P., Cheng, J., et al.: Interval multi-objective quantum-inspired cultural algorithms. Neural Comput. Appl. 1–14 (2016)Google Scholar
- 16.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_39CrossRefGoogle Scholar
- 17.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)Google Scholar
- 18.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