Title
Artificial immune systems for Artificial Olfaction data analysis: Comparison between AIRS and ANN models
Abstract
Artificial Olfaction (AO) data analysts have gained long term experience on nervous system based machine learning metaphors such as Artificial Neural Networks. In this work we propose and evaluate the use of a novel tool based on an emerging, however, powerful metaphor: the Artificial Immune Systems (AIS). AIS models were developed in the `90s; ever since they have reached significant maturity, and were to show good performance in both explorative data analysis and classification tasks. After selecting different artificial olfaction databases, we compare the utility of classic Back-Propagation Neural Network (BPNN) models with Artificial Immune Recognition Systems (AIRS) algorithms for classification problems, discussing its architectural strengths and weaknesses. Although BPNN retained a slight performance advantage on the investigated datasets, we were able to show that the AIS metaphor can express interesting characteristics for artificial olfaction data analysis. As an example, in a preliminary setup, the AIRS classifier showed superior performance when the sensor signals are affected by drift.
Year
DOI
Venue
2010
10.1109/IJCNN.2010.5596599
Neural Networks
Keywords
Field
DocType
artificial immune systems,backpropagation,chemioception,data analysis,medical administrative data processing,neural nets,neurophysiology,pattern classification,AIRS classifier,artificial immune recognition systems algorithms,artificial neural networks,artificial olfaction data analysis,artificial olfaction databases,backpropagation neural network models,nervous system based machine learning metaphors
Artificial immune system,Olfaction,Computer science,Artificial intelligence,Artificial neural network,Statistical classification,Backpropagation,Classifier (linguistics),Machine learning
Conference
ISSN
ISBN
Citations 
1098-7576
978-1-4244-6916-1
2
PageRank 
References 
Authors
0.37
5
9