ELM-Based Ensemble Classifier for Gas Sensor Array Drift Dataset

  • D. Arul Pon Daniel
  • K. Thangavel
  • R. Manavalan
  • R. Subash Chandra Boss
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 246)


Much work has been done on classification for the past fifteen years to develop adapted techniques and robust algorithms. The problem of data correction in the presence of simultaneous sources of drift, other than sensor drift, should also be investigated, since it is often the case in practical situations. ELM is a competitive machine learning technique, which has been applied in different domains for classification. In this paper, ELM with different activation functions has been implemented for gas sensor array drift dataset. The experimental results show that the ELM with bipolar function classifies the drift dataset with an average accuracy of 96 % than the other function. The proposed method is compared with SVM.


ELM Ensembles Gas sensor array drift dataset Bipolar 



The first and fourth author immensely acknowledges the partial financial assistance under University Research Fellowship, Periyar University, Salem.

The second author immensely acknowledges the UGC, New Delhi, for partial financial assistance under UGC-SAP (DRS) Grant No. F.3-50/2011.


  1. 1.
    Sofiane Brahim-Belhouari, Amine Bermak and Philip C. H. Chan (2004) Gas Identification with Microelectronic Gas Sensor in Presence of Drift Using Robust GMM. IEEE ICASSP 2004, 0-7803-8484-9/04/$20.00, pp. V-833–V-836.Google Scholar
  2. 2.
    Arul Pon Daniel D, Thangavel K, and Subash Chandra Boss R (2012) A Review of Early De-tection of Cancers using Breath Analysis. Proc. IEEE Conf. Pattern Recognition, Infor-matics and Mobile Engineering (PRIME 2012), IEEE Press, DOI:  10.1109/ICPRIME.2013.6208385: 433–438.
  3. 3.
    John-Erik Haugen, Oliver Tomic, Knut Kvaal (1999) A calibration method for handling the temporal drift of solid state gas-sensors. Analytica Chimica Acta, pp. 23–39.Google Scholar
  4. 4.
    Persaud K, Dodd G (1982) Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature 299 (5881):352–355.Google Scholar
  5. 5.
    Belusov AI, Verkazov SA, von Frese J (2002) Applicational aspects of support vector machines. J Chemometric, 16(8–12):482–489.Google Scholar
  6. 6.
    Huang G B, Zhu Q Y, Siew C K (2004) Extreme learning machine: A new learning scheme of feedforward neural networks. In Proceedings of International Joint Conference on Neural Networks, Budapest, Hungary, 2:985–990.Google Scholar
  7. 7.
    Alexander Vergara, Shankar Vembu, Tuba Ayhan, Margaret A. Ryan, Margie L. Homer and Ramón Huerta (2012) Chemical gas sensor drift compensation using classifier ensembles. Sensors and Actuators B: Chemical, DOI:  10.1016/j.snb.2012.01.074: 320–329.
  8. 8.
    Huang G, Zhu Q, Siew C (2006) Exreme Learning Machine: Theory and applications. Neuro-computing. 70(1-3):489–501.Google Scholar
  9. 9.
    Brady M, Highnam R (1999) Mammographic Image Analysis. Kluwer series on medical image Understanding.Google Scholar
  10. 10.
    Hassanien (2007) Fuzzy rough sets hybrid scheme for breast cancer detection. Image and Vision Computing, 25(2):172–183.Google Scholar
  11. 11.
    Roffilli M (2006) Advanced machine learning techniques for digital mammography. Technical Report UBLCS, University of Bologna. Italy.Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  • D. Arul Pon Daniel
    • 1
  • K. Thangavel
    • 1
  • R. Manavalan
    • 2
  • R. Subash Chandra Boss
    • 1
  1. 1.Department of Computer SciencePeriyar UniversitySalemIndia
  2. 2.Department of Computer ApplicationKSR Arts and Science CollegeTrichengoduIndia

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