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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)

Abstract

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.

Keywords

ELM Ensembles Gas sensor array drift dataset Bipolar 

Notes

Acknowledgments

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.

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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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