Artificial Intelligence Review - Special issue on lazy learning
Evolutionary Algorithms in Engineering Applications
Evolutionary Algorithms in Engineering Applications
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
MACLAW: A modular approach for clustering with local attribute weighting
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Application areas of AIS: the past, the present and the future
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Effective diagnosis of heart disease through neural networks ensembles
Expert Systems with Applications: An International Journal
A meta-heuristic approach for improving the accuracy in some classification algorithms
Computers and Operations Research
International Journal of Computational Intelligence Studies
Expert Systems with Applications: An International Journal
A novel hybrid neural learning algorithm using simulated annealing and quasisecant method
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
A hybrid intelligent system for medical data classification
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
An increasing number of algorithms and applications have coming into scene in the field of artificial immune systems (AIS) day by day. Whereas this increase is bringing successful studies, still, AIS is not an effective problem solver in some problem fields such as classification, regression, pattern recognition, etc. So far, many of the developed AIS algorithms have used a distance or similarity measure as the case in instance based learning (IBL) algorithms. The efficiency of IBL algorithms lies mainly in the weighting scheme they used. This weighting idea was taken as the objective of our study in that we used genetic algorithms to determine the weights of attributes and then used these weights in our previously developed Artificial Immune System (AWAIS). We evaluated the performance of new configuration (GA-AWAIS) on two medical datasets which were Statlog Heart Disease and BUPA Liver Disorders dataset. We also compared it with AWAIS for those problems. The obtained classification accuracy was very good with respect to both AWAIS and other common classifiers in literature.