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.
Highlights
Artificial Immune System (AIS) is a computational method inspired by the biology immune system
To show the capability of AIS to do the classification was the initial objective of developing Artificial Immune Recognition System (AIRS), but results shown that AIRS is comparable with famous classifiers
The steps are as follows of different immune system theories. (Watkins and Boggess, 2002; Watkins et al, 2004): As AIRS is resource limited artificial immune system, this concept i.e., “resource limited” is explained
Summary
Artificial Immune System (AIS) is a computational method inspired by the biology immune system It is progressing slowly and steadily as a new branch of computational intelligence and soft computing (de Castro and Timmis, 2002; de Castro and Timmis, 2003; Golzari et al, 2009). It has been used in several applications such as machine learning, pattern recognition, computer virus detection, anomaly detection, optimization and genre classification (de Castro and Timmis, 2002; de France et al, 2005; Igawa and Ohashi, 2007; Kim et al, 2007; Doraisamy and Golzari, 2010). AIRS is probably the first and best known AIS for classification, having been developed 2001Watkins and Boggess (2002)
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call