Improved SVM Classifier Incorporating Adaptive Condensed Instances Based on Hybrid Continuous-Discrete Particle Swarm Optimization


  • Chun-Liang Lu Department of Applied Information and Multimedia, Ching Kuo Institute of Management and Health, Keelung, Taiwan
  • Tsun-Chen Lin Department of Computer and Communication Engineering, Dahan Institute of Technology, Hualien, Taiwan


Hybrid particle swarm optimization (HPSO), Adaptive condensed instances (ACI), Support vector machine (SVM)


In recent years, support vector machine (SVM) based on empirical risk minimization is supervised learning model which has been successfully used in the classification and regression. The standard soft-margin SVM trains a classifier by solving an optimization problem to decide which instances of the training data set are support vectors. However, in many real applications it is imperative to perform feature selection to detect which features are actually relevant. In order to further improve the performance, we propose the adaptive condensed instances (ACI) strategy based on the hybrid particle swarm optimization (HPSO) algorithm for the SVM classifier design. The basic idea of the proposed method is to adopt HPSO to simultaneously optimize the ACI and SVM kernel parameters for the classification accuracy enhancement. The numerical experiments on several UCI benchmark datasets are conducted to find the optimal parameters for building the SVM model. Experiment results show that the proposed framework can achieve better performance than other published methods in literature and provide a simple but subtle strategy to effectively improve the classification accuracy for SVM classifier.


V. N. Vapnik, "An overview of statistical learning theory," IEEE Transactions on Neural Networks, vol. 10, pp. 988-999, 1999.

I.J. Ding, C.T. Yen, and D.C. Ou "A Method to Integrate GMM, SVM and DTW for Speaker Recognition," International Journal of Engineering and Technology Innovation, vol. 4, pp. 38-47, 2014.

R. Kothandan and S. Biswas, "Identifying microRNAs involved in cancer pathway using support vector machines," Computational Biology and Chemistry, vol. 55, pp. 31-36, Apr. 2015.

B. Ramesh and J.G.R. Sathiaseelan, "An Advanced Multi Class Instance Selection based Support Vector Machine for Text Classification," Procedia Computer Science, vol. 57, pp. 1124-1130, 2015.

X. Zhang, G. Wu, Z. Dong and C. Crawford, "Embedded feature-selection support vector machine for driving pattern recognition," Journal of the Franklin Institute, vol. 352, pp. 669-685, 2015.

S.W. Lin, Z.J. Lee, S.C. Chen and T.Y. Tseng, "Parameter determination of support vector machine and feature selection using simulated annealing approach," Applied Soft Computing, vol. 8, pp. 1505-1512, 2008.

C.L. Lu, I.F. Chung and T.C. Lin, "The Hybrid Dynamic Prototype Construction and Parameter Optimization with Genetic Algorithm for Support Vector Machine," International Journal of Engineering and Technology Innovation, vol. 5, pp. 220-232, 2015.

B. Sahu and D. Mishra, "A Novel Feature Selection Algorithm using Particle Swarm Optimization for Cancer Microarray Data," Procedia Engineering, vol. 38, pp. 27-31, 2012.

C.L. Huang and C.J. Wang, “A GA-based feature selection and parameters optimization for support vector machines,” Expert systems with applications, vol. 31, pp. 231-240, 2006.

J. Kennedy and R. C. Eberhart, "A discrete binary version of the particle swarm algorithm," In Proceedings of the World on Systematics, Cybernetics and Informatics, vol. 8, pp. 4104-4109, 1997.

V. Vapnik, The Nature of Statistical Learning Theory. Berlin, Germany: Springer-Verlag, 1995.

C.C. Chang, and C.J. Lin, "Training nu-support vector regression: theory and algorithms," Neural Computation, vol. 14, pp. 1959-1977, 2002.




How to Cite

C.-L. Lu and T.-C. Lin, “Improved SVM Classifier Incorporating Adaptive Condensed Instances Based on Hybrid Continuous-Discrete Particle Swarm Optimization”, Adv. technol. innov., vol. 1, no. 2, pp. 53–57, Sep. 2016.