Classifier Dependent Dimensionality Reduction for Resource Restricted Environments

  • Divyanshu Kalra
  • Chaitanya Dwivedi
  • Swati AggarwalEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 799)


High dimensionality problems have become prevalent in present day machine learning applications. The voluminous datasets acquired from sources like cameras, spectroscopes, and other sensors need to be analysed and modelled in a way that uses the available computational resources most efficiently. The paper proposes a genetic algorithm optimised neural network model that takes care of the issue mentioned above. A comparison is also drawn between the results produced by the proposed model and those produced by other contemporary dimensionality reduction algorithms.


Genetic algorithm Neural networks t-SNE Dimensionality reduction 


  1. 1.
    Ishibuchi, H., Nakashima, T., Murata, T.: Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans. Syst. Man Cybern. B Cybern. 29(5), 601–618 (1999)CrossRefGoogle Scholar
  2. 2.
    sorend, sorend/fylearn, GitHub. Accessed 30 Jan 2017
  3. 3.
    Jang, J.-S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)CrossRefGoogle Scholar
  4. 4.
    Obaidat, M.S., Sadoun, B.: Verification of computer users using keystroke dynamics. IEEE Trans. Syst. Man Cybern. B Cybern. 27(2), 261–269 (1997)CrossRefGoogle Scholar
  5. 5.
    Jamieson, A.R., Giger, M.L., Drukker, K., Li, H., Yuan, Y., Bhooshan, N.: Exploring nonlinear feature space dimension reduction and data representation in breast Cadx with Laplacian eigenmaps and t-SNE. Med. Phys. 37(1), 339–351 (2010)CrossRefGoogle Scholar
  6. 6.
    Herrera, F.: Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol. Intell. 1(1), 27–46 (2008)CrossRefGoogle Scholar
  7. 7.
    Derrac, J., Cornelis, C., Garca, S., Herrera, F.: Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection. Inf. Sci. 186(1), 73–92 (2012)CrossRefGoogle Scholar
  8. 8.
    Pedrycz, W.: Granular Computing: Analysis and Design of Intelligent Systems. CRC Press, Boca Raton (2016)Google Scholar
  9. 9.
    Jin, Y.: Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement. IEEE Trans. Fuzzy Syst. 8(2), 212–221 (2000)CrossRefGoogle Scholar
  10. 10.
    Wang, D., Zeng, X.-J., Keane, J.A.: Simplified structure evolving method for Mamdani fuzzy system identification and its application to high-dimensional problems. Inf. Sci. 220, 110–123 (2013)CrossRefGoogle Scholar
  11. 11.
    Coello, C.C., Lamont, G.B., van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Springer Science & Business Media, New York (2007). Scholar
  12. 12.
    Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Hoboken (2001)zbMATHGoogle Scholar
  13. 13.
    Fazzolari, M., Alcala, R., Nojima, Y., Ishibuchi, H., Herrera, F.: Review of the application of multiobjective evolutionary fuzzy systems: current status and further directions. IEEE Trans. Fuzzy Syst. 21(1), 45–65 (2013)CrossRefGoogle Scholar
  14. 14.
    Azar, A.T., Hassanien, A.E.: Dimensionality reduction of medical big data using neural-fuzzy classifier. Soft. Comput. 19(4), 1115–1127 (2014)CrossRefGoogle Scholar
  15. 15.
    Tomaev, N., Buza, K.: Hubness-aware kNN classification of high-dimensional data in presence of label noise. Neurocomputing 160, 157–172 (2015)CrossRefGoogle Scholar
  16. 16.
    Mansoori, E.G., Shafiee, K.S.: On fuzzy feature selection in designing fuzzy classifiers for high-dimensional data. Evol. Syst. 7(4), 255–265 (2015)CrossRefGoogle Scholar
  17. 17.
    Olson, D.L., Wu, D.: Data sets. Predictive Data Mining Models. CRM, pp. 9–15. Springer, Singapore (2017). Scholar
  18. 18.
    Killourhy, K.S., Maxion, R.A.: Comparing anomaly-detection algorithms for keystroke dynamics. In: 2009 IEEE/IFIP International Conference on Dependable Systems & Networks (2009)Google Scholar
  19. 19.
    Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  20. 20.
    Gorman, R.P., Sejnowski, T.J.: Analysis of hidden units in a layered network trained to classify sonar targets. Neural Netw. 1(1), 75–89 (1988)CrossRefGoogle Scholar
  21. 21.
    Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(2), 179–188 (1936)CrossRefGoogle Scholar
  22. 22.
    Parekh, V.S., Jacobs, J.R., Jacobs, M.A.: Unsupervised Non Linear Dimensionality Reduction Machine Learning methods applied to Multiparametric MRI in cerebral ischemia: Preliminary Results, arXiv [cs.CV], 13 June 2016Google Scholar
  23. 23.
    Petscharnig, S., Lux, M., Chatzichristofis, S.: Dimensionality reduction for image features using deep learning and autoencoders. In: Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing, Florence, Italy, pp. 23:1–23:6 (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Divyanshu Kalra
    • 1
  • Chaitanya Dwivedi
    • 1
  • Swati Aggarwal
    • 1
    Email author
  1. 1.Netaji Subhas Institue of TechnologyDwarkaIndia

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