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Genetic Programming Based on Granular Computing for Classification with High-Dimensional Data

  • Wenbin PeiEmail author
  • Bing Xue
  • Lin Shang
  • Mengjie Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11320)

Abstract

Classification tasks become more challenging when having the curse of dimensionality issue. Recently, there has been an increasing number of datasets with thousands of features. Some classification algorithms often need feature selection to avoid the curse of dimensionality. Genetic programming (GP) has shown success in classification tasks. GP does not require to do feature selection because of its built-in capability to automatically select informative features. However, GP-based methods are often computationally intensive to achieve a good classification accuracy. Based on perspectives from granular computing (GrC), this paper proposes a new approach to linking features hierarchically for GP-based classification. Experiments on seven high-dimensional datasets show the effectiveness of the proposed algorithm in terms of saving training time and enhancing the classification accuracy, compared to baseline methods.

Keywords

High-dimensional data Genetic programming Granular computing Classification 

Notes

Acknowledgement

This work was supported in part by the Marsden Fund of New Zealand Government under Contracts VUW1209, VUW1509 and VUW1615, Huawei Industry Fund E2880/3663, Natural Science Foundation of Jiangsu, China BK20161406, and the University Research Fund at Victoria University of Wellington 209862/3580, and 213150/3662.

References

  1. 1.
    Bargiela, A., Pedrycz, W.: Granular computing. In: Handbook in Computational Intelligence. Fuzzy Logic, Systems, Artificial Neural Networks, and Learning Systems, vol. 1, pp. 43–66. World Scientific (2016)Google Scholar
  2. 2.
    Cao, J., Lin, Z., Huang, G.B., Liu, N.: Voting based extreme learning machine. Inf. Sci. 185(1), 66–77 (2012)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Cervante, L., Xue, B., Shang, L., Zhang, M.: A dimension reduction approach to classification based on particle swarm optimisation and rough set theory. In: Thielscher, M., Zhang, D. (eds.) AI 2012. LNCS (LNAI), vol. 7691, pp. 313–325. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-35101-3_27CrossRefGoogle Scholar
  4. 4.
    Deng, Z., Zhu, X., Cheng, D., Zong, M., Zhang, S.: Efficient knn classification algorithm for big data. Neurocomputing 195, 143–148 (2016)CrossRefGoogle Scholar
  5. 5.
    Espejo, P.G., Ventura, S., Herrera, F.: A survey on the application of genetic programming to classification. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(2), 121–144 (2010)CrossRefGoogle Scholar
  6. 6.
    Fleury, A., Vacher, M., Noury, N.: SVM-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results. IEEE Trans. Inf. Technol. Biomed. 14(2), 274–283 (2010)CrossRefGoogle Scholar
  7. 7.
    Joshi, A., Dangra, J., Rawat, M.: A decision tree based classification technique for accurate heart disease classification and prediction. Int. J. Technol. Res. Manag. 3, 1–4 (2016)Google Scholar
  8. 8.
    Luna, J.M., Pechenizkiy, M., del Jesus, M.J., Ventura, S.: Mining context-aware association rules using grammar-based genetic programming. IEEE Trans. Cybern. (2017)Google Scholar
  9. 9.
    Murphy, K.P.: Naive Bayes Classifiers. University of British Columbia (2006)Google Scholar
  10. 10.
    Nguyen, H.B., Xue, B., Andreae, P.: A hybrid GA-GP method for feature reduction in classification. In: Shi, Y., et al. (eds.) SEAL 2017. LNCS, vol. 10593, pp. 591–604. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68759-9_48CrossRefGoogle Scholar
  11. 11.
    Poli, R., Langdon, W., McPhee, N.: A Field Guide to Genetic Programming (2008)Google Scholar
  12. 12.
    Sáez, J.A., Galar, M., Luengo, J., Herrera, F.: Tackling the problem of classification with noisy data using multiple classifier systems: analysis of the performance and robustness. Inf. Sci. 247, 1–20 (2013)CrossRefGoogle Scholar
  13. 13.
    Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4), 427–437 (2009)CrossRefGoogle Scholar
  14. 14.
    Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P.: Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43(6), 1947–1958 (2003)CrossRefGoogle Scholar
  15. 15.
    Thearling, K.: An Introduction to Data Mining (2017)Google Scholar
  16. 16.
    Tran, B., Xue, B., Zhang, M.: Genetic programming for feature construction and selection in classification on high-dimensional data. Memetic Comput. 8(1), 3–15 (2016)CrossRefGoogle Scholar
  17. 17.
    Tran, B., Xue, B., Zhang, M.: Using feature clustering for GP-based feature construction on high-dimensional data. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds.) EuroGP 2017. LNCS, vol. 10196, pp. 210–226. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55696-3_14CrossRefGoogle Scholar
  18. 18.
    Wang, G., Yang, J., Xu, J.: Granular computing: from granularity optimization to multi-granularity joint problem solving. Granul. Comput. 2(3), 105–120 (2017)CrossRefGoogle Scholar
  19. 19.
    Yang, H.J., Roe, B.P., Zhu, J.: Studies of stability and robustness for artificial neural networks and boosted decision trees. Nucl. Instrum. Methods Phys. Res. Sect. A: Accel. Spectrometers Detect. Assoc. Equip. 574(2), 342–349 (2007)CrossRefGoogle Scholar
  20. 20.
    Yao, J.: Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation. IGI Global (2010)Google Scholar
  21. 21.
    Yao, Y.: A triarchic theory of granular computing. Granul. Comput. 1(2), 145–157 (2016)CrossRefGoogle Scholar
  22. 22.
    Zhang, G.P.: Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 30(4), 451–462 (2000)CrossRefGoogle Scholar
  23. 23.
    Zhu, Z., Ong, Y.S., Dash, M.: Markov blanket-embedded genetic algorithm for gene selection. Pattern Recognit. 40(11), 3236–3248 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wenbin Pei
    • 1
    Email author
  • Bing Xue
    • 1
  • Lin Shang
    • 2
  • Mengjie Zhang
    • 1
  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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