Advertisement

Semi-Supervised Learning with the Integration of Fuzzy Clustering and Artificial Neural Network

  • Indrajit Saha
  • Nivriti Debnath
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 734)

Abstract

Supervised and unsupervised learning are different types of machine learning approaches that are used for pattern classification and clustering. Supervised learning finds the nearest matching by getting the knowledge from labeled training data whereas unsupervised learning does not acquire any knowledge of labeled data to produce output. In extensive research, it has been shown that to cluster huge amount of unlabeled data, the combination of both supervised and unsupervised learning provides more accuracy than the other conventional learning methods, which is the main concept of Semi-Supervised learning. In case of unsupervised learning, Fuzzy C-Means (FCM) is a well-known technique that uses membership matrix while doing clustering. The membership matrix can be used to identify uncertain points which are belonging to all the clusters with low or equal degree of membership. This fact motivated us to propose a Semi-Supervised technique with the integration of FCM and Artificial Neural Network (ANN). In this regard, ANN is trained twice with the certain amount of high membership and ground truth points. Thereafter, rest of the points is classified using such trained ANN. Here nine different well-known training functions are used in ANN, while top four are reported for different datasets. The result of the proposed method is compared with the other state-of-the-art methods for different artificial and real life datasets. For this purpose, two well-known metrics called Adjusted Rand Index and Minkowski Score are computed and reported quantitatively and visually. Moreover, the statistical significance of the results produced by the proposed method is justified using independent two-sample t-test.

Keywords

Artificial Neural Network Fuzzy clustering Semi-Supervised learning Statistical test 

Notes

Acknowledgements

This work was partially supported by the grants from the Department of Science and Technology, India (DST/INT/Pol/P-36/2016).

References

  1. 1.
    Zhu, X.: Semi-supervised learning literature survey. Technical report. University of Wiconsin (2005)Google Scholar
  2. 2.
    Chakraborty, U., Maulik, U.: Identifying cancer biomarkers from microarray data using feature selection and semisupervised learning. IEEE J. Transl. Eng. Health Med. 2, 1–11 (2014)CrossRefGoogle Scholar
  3. 3.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms, 1st edn. Plenum Press, New York (1981)CrossRefzbMATHGoogle Scholar
  4. 4.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)CrossRefGoogle Scholar
  5. 5.
    Moller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993)CrossRefGoogle Scholar
  6. 6.
    Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE International Proceedings on Neural Networks, pp. 586–591 (1993)Google Scholar
  7. 7.
    Scales, L.E.: Introduction to Non-Linear Optimization. Springer, New York (1985)CrossRefGoogle Scholar
  8. 8.
    Powell, M.J.D.: Restart procedures for the conjugate gradient method. Math. Program. 12, 241–254 (1977)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Gill, P.E., Murray, W., Wright, M.H.: Practical Optimization. Academic Press, New York (1981)zbMATHGoogle Scholar
  10. 10.
    Battiti, R.: First and second order methods for learning: Between steepest descent and Newton’s method. Neural Comput. 4(2), 141–166 (1992)CrossRefGoogle Scholar
  11. 11.
    Yeung, K.Y., Ruzzo, W.L.: An empirical study on principal component analysis for clustering gene expression data. Bioinformatics 17(9), 763–774 (2001)CrossRefGoogle Scholar
  12. 12.
    Jardine, N., Sibson, R.: Mathematical Taxonomy. Wiley, New York (1971)zbMATHGoogle Scholar
  13. 13.
    Maulik, U., Saha, I.: Modified differential evolution based fuzzy clustering for pixel classification in remote sensing imagery. Pattern Recognit. 42(9), 2135–2184 (2009)CrossRefzbMATHGoogle Scholar
  14. 14.
    Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric Statistical Methods, 3rd edn. Wiley, New York (2014)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technical Teachers’ Training and ResearchKolkataIndia

Personalised recommendations