@article {10.3844/jcssp.2011.148.153,
article_type = {journal},
title = {An Efficient and Effective Immune Based Classifier},
author = {Golzari, Shahram and Doraisamy, Shyamala and Sulaiman, Md Nasir and Udzir, Nur Izura},
volume = {7},
number = {2},
year = {2011},
month = {Feb},
pages = {148-153},
doi = {10.3844/jcssp.2011.148.153},
url = {https://thescipub.com/abstract/jcssp.2011.148.153},
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 = {Journal of Computer Science},
publisher = {Science Publications}
}