Username   Password       Forgot your password?  Forgot your username? 


Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018
No.6 June 2018
No.6 June 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006


Relief Feature Selection and Parameter Optimization for Support Vector Machine based on Mixed Kernel Function

Volume 14, Number 2, February 2018, pp. 280-289
DOI: 10.23940/ijpe.18.02.p9.280289

Wei Zhanga,b, Junjie Chenb

aInformation Center, Shanxi Medical College for Continuing Education, Taiyuan, 030012, China
bCollege of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China


In order to improve the classification performance of Support Vector Machine (SVM), Relief feature selection algorithm was used to obtain the most relevant feature subset and remove redundant features. The mixed kernel function, which combined the global kernel function with the local kernel function, was proposed to strengthen the learning ability and generalization performance of SVM. In addition, the parameter optimization of SVM, which combined Genetic Algorithm (GA) with grid search, was performed to reduce computation time and find optimal solutions. Finally, the methods presented in this paper were used in the Heart disease data set and the Breast cancer data set in the UCI. Compared with KNN and BP neural network, the classification result of SVM model with Relief algorithm and mixed kernel function significantly outperformed the other comparable classification model and the experimental results demonstrate the validity of the proposed model.


References: 31

    1. V. Bolón-Canedo, N. Sánchez-Maroño, A. Alonso-Betanzos, J. M. Benítez, and F. Herrera, “A Review of Microarray Datasets and Applied Feature Selection Methods,” Information Sciences, vol.282, no.5, pp.111-135, 2014.
    2. Y. C. Chen and C. T. Su, “Distance-based Margin Support Vector Machine for Classification,” Applied Mathematics and Computation, vol.283, no.12, pp.141-152, 2016.
    3. J. Dhalia Sweetlin, H. Khanna Nehemiah, and A. Kannan, “Feature Selection Using Ant Colony Optimization with Tandem-Run Recruitment to Diagnose Bronchitis from CT Scan Images,” Computer Methods and Programs in Biomedicine, vol.145, no.7, pp.115-125, 2017.
    4. D. D. Du, X. L. Jia, and C. B. Hao, “A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction,” Mathematical Problems in Engineering vol.2016, Article ID 4615903, 8 pages,2016
    5. S. Foithong, O. Pinngern, and B. Attachoo, “Feature Subset Selection Wrapper Based on Mutual Information and Rough Sets,” Expert Systems with Applications, vol. 39, no.1, pp. 574-584, 2012.
    6. T. S. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, and M. Schummer, “Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data,” Bioinformatics, vol. 16, no. 10, pp. 906–914,2000.
    7. J. B. Geng, L. K. Sun, and S. X. Chen, “Parameters Optimization of Combined Kernel Function for Support Vector Machine,” Journal of Computer Applications, vol.33, no.5, pp.1321-1323,1356, 2013.
    8. B. Gu, G. S. Zheng, and J. D. Wang, “Analysis for Incremental and Decremental Standard Support Vector Machine,” Journal of Software, vol.24, no.7, pp.1601-1613,2013.
    9. J. P. Hua, W. D. Tembe, and E. R. Dougherty, “Performance of Feature Selection Methods in the Classification of High-Dimension Data,” Pattern Recognition, vol. 42, no.3, pp. 409-424, 2009.
    10. F. Kang, J. S. Li, and J. J. Li, “System Reliability Analysis of Slopes Using Least Squares Support Vector Machines with Particle Swarm Optimization,” Neurocomputing, vol.209, no.15, pp.46–56, 2016.
    11. K. Li, Y. Wu, Y. Nan, P. Li, and Y. Li, “Hierarchical Multi-Class Classification in Multimodal Spacecraft Data Using DNN and Weighted Support Vector Machine,” Neurocomputing, vol.259, no.15, pp.55-65, 2017.
    12. S. Li, L. W. Li, D. F. Zhuang, and Y. Wang, “Research on Mixed Kernel Function and Its Application in the Field of Data Modeling,” Computer Simulations, vol.32, no.7, pp.1-6, 2015.
    13. T. W. Liao, “Feature Extraction and Selection from Acoustic Emission Signals with an Application in Grinding Wheel Condition Monitoring,” Engineering Applications of Artificial Intelligence, vol. 23, no. 1, pp. 74–84, 2010.
    14. K.C. Lin, S. Y. Chen, and J. Hung, “Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms,” Mathematical Problems in Engineering, vol.2015, Article ID 604108, 9 pages,2015.
    15. C. Liu, W. Y. Wang, Q. Zhao, X. M. Shen, and M. Konan, “A New Feature Selection Method Based on a Validity Index of Feature Subset,” Pattern Recognition Letters, vol.92, no.6, pp.1-8,2017.
    16. X. L. Liu, D. X. Jia, and H. Li, “Research on Kernel Parameter Optimization of Support Vector Machine in Speaker Recognition,” Science Technology and Engineering, vol. 10, no.7, pp. 1669-1673, 2010.
    17. Y. Liu, J. W. Bi, and Z. P. Fan, “A Method for Multi-Class Sentiment Classification Based on an Improved One-Vs-One (OVO) Strategy and the Support Vector Machine (SVM) Algorithm,” Information Sciences, vol.394-395, no.20, pp.38-52,2017.
    18. H. J. Lu, J. Y. Chen, K. Yan, Q. Jin, Y. Xue, and Z. G. Gao, “A Hybrid Feature Selection Algorithm for Gene Expression Data Classification,” Neurocomputing, vol.256, no.14, pp.56-62,2017.
    19. J. Qu, H. Chen, W. Z. Liu, Z. Li, and B. Zhang, “Application of Support Vector Machine Based on Improved Grid Search in Quantitative Analysis of Gas,” Chinese Journal of Sensors and Actuators, vol.28, no.5, pp.774-778,2015
    20. M. R. G. Raman, N. Somu, K. Kirthivasan, R. Liscano, and V.S.S. Sriram, “An Efficient Intrusion Detection System Based on Hypergraph-Genetic Algorithm for Parameter Optimization and Feature Selection in Support Vector Machine,” Knowledge-Based Systems, In Press, Corrected Proof, Available online 6 July 2017, pp.1-12, 2017.
    21. B. K. Singh, K. Verma, A. S. Thoke, and J. S. Suri, “Risk Stratification of 2D Ultrasound-Based Breast Lesions Using Hybrid Feature Selection in Machine Learning Paradigm,” Measurement, vol.105, no.4, pp.146-157,2017.
    22. Q. J. Song, H.Y. Jiang, and J. Liu, “Feature Selection Based on FDA and F-Score for Multi-Class Classification,” Expert Systems with Applications, vol. 81, no. C, pp. 22–27, 2017.
    23. S. Szedmak, J. Shawe-Taylor, C. Saunders, and D. Hardoon, “Multiclass Classification by L1 Norm Support Vector Machine,” in Proceedings of the Pattern Recognition and Machine Learning in Computer Vision Workshop, pp. 1-19, Grenoble, France, 2004.
    24. A. Tharwat, A. E. Hassanien, and B. E. Elnaghi, “A BA-Based Algorithm for Parameter Optimization of Support Vector Machine,” Pattern Recognition Letters, vol.93, no.7, pp.13-22, 2017.
    25. Uncu and L. B. Türken, “A Novel Feature Selection Approach: Combining Features Wrappers and Filters,” Information Sciences, vol. 177, no.2, pp. 449-466, 2007.
    26. V. N. Vapnik. Statistical Learning Theory, New York, USA: Wiley Interscience, 1998.
    27. Y. L. Wu, Q. He, and T. W. Xu, “Application of Improved Adaptive Particle Swarm Optimization Algorithm in WSN Coverage Optimization,” Chinese Journal of Sensors and Actuators, vol. 29, no. 4, pp. 559-565, 2016.
    28. X. Yang, H. Peng, and M. Shi, “SVM with Multiple Kernels Based on Manifold Learning for Breast Cancer Diagnosis,” In: Proceeding of 2013 IEEE International Conference on Information and Automation (ICIA), IEEE Press, Yinchuang, China, pp. 396–399,2013.
    29. X. Zhang, Z. H. Deng, S. T. Wang, and J. S. Cai, “Maximum Entropy Relief Feature Weighting,” Journal of Computer Research and Development, vol. 48, no.6, pp. 1038-1048, 2011.
    30. X. L. Zhang, W. Chen, B. J. Wang, and X.F. Chen, “Intelligent Fault Diagnosis of Rotating Machinery Using Support Vector Machine with Ant Colony Algorithm for Synchronous Feature Selection and Parameter Optimization,” Neurocomputing, vol.167, no.16, pp.260-279,2015.
    31. X. L. Zhang, X. F. Chen, and Z. J. He, “An ACO-based Algorithm for Parameter Optimization of Support Vector Machines,” Expert Systems with Applications, vol.37, no.9, pp.6618-6628,2010.


      Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

      Download this file (IJPE-2018-02-09.pdf)IJPE-2018-02-09.pdf[Relief Feature Selection and Parameter Optimization for Support Vector Machine based on Mixed Kernel Function]391 Kb


      Prev Next

      Temporal Multiscale Consumption Strategies of Intermittent Energy based on Parallel Computing

      Huifen Chen, Yiming Zhang, Feng Yao, Zhice Yang, Fang Liu, Yi Liu, Zhiheng Li, and Jinggang Wang

      Read more

      Decision Tree Incremental Learning Algorithm Oriented Intelligence Data

      Hongbin Wang, Ci Chu, Xiaodong Xie, Nianbin Wang, and Jing Sun

      Read more

      Spark-based Ensemble Learning for Imbalanced Data Classification

      Jiaman Ding, Sichen Wang, Lianyin Jia, Jinguo You, and Ying Jiang

      Read more

      Classification Decision based on a Hybrid Method of Weighted kNN and Hyper-Sphere SVM

      Peng Chen, Guoyou Shi, Shuang Liu, Yuanqiang Zhang, and Denis Špelič

      Read more

      An Improved Algorithm based on Time Domain Network Evolution

      Guanghui Yan, Qingqing Ma, Yafei Wang, Yu Wu, and Dan Jin

      Read more

      Auto-Tuning for Solving Multi-Conditional MAD Model

      Feng Yao, Yi Liu, Huifen Chen, Chen Li, Zhonghua Lu, Jinggang Wang, Zhiheng Li, and Ningming Nie

      Read more

      Smart Mine Construction based on Knowledge Engineering and Internet of Things

      Xiaosan Ge, Shuai Su, Haiyang Yu, Gang Chen, and Xiaoping Lu

      Read more

      A Mining Model of Network Log Data based on Hadoop

      Yun Wu, Xin Ma, Guangqian Kong, Bin Wang, and Xinwei Niu

      Read more
      This site uses encryption for transmitting your passwords.