Research Article Open Access

An Efficient and Effective Immune Based Classifier

Shahram Golzari, Shyamala Doraisamy, Md Nasir Sulaiman and Nur Izura Udzir

Abstract

Problem statement: Artificial Immune Recognition System (AIRS) is most popular and effective immune inspired classifier. Resource competition is one stage of AIRS. Resource competition is done based on the number of allocated resources. AIRS uses a linear method to allocate resources. The linear resource allocation increases the training time of classifier. Approach: In this study, a new nonlinear resource allocation method is proposed to make AIRS more efficient. New algorithm, AIRS with proposed nonlinear method, is tested on benchmark datasets from UCI machine learning repository. Results: Based on the results of experiments, using proposed nonlinear resource allocation method decreases the training time and number of memory cells and doesn't reduce the accuracy of AIRS. Conclusion: The proposed classifier is an efficient and effective classifier.

Journal of Computer Science
Volume 7 No. 2, 2011, 148-153

DOI: https://doi.org/10.3844/jcssp.2011.148.153

Submitted On: 31 October 2010 Published On: 25 February 2011

How to Cite: Golzari, S., Doraisamy, S., Sulaiman, M. N. & Udzir, N. I. (2011). An Efficient and Effective Immune Based Classifier. Journal of Computer Science, 7(2), 148-153. https://doi.org/10.3844/jcssp.2011.148.153

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Keywords

  • Artificial Immune Recognition System (AIRS)
  • resource allocation
  • Artificial Immune System (AIS)
  • clonal selection
  • Artificial Recognition Ball (ARB)
  • nonlinear resource allocation
  • EXPAIRS generates
  • feature vector