An artificial immune system algorithm for classification tasks. An electronic nose case study

Abstract

This study explores the application of an electronic nose (e-nose), employing an Artificial Immune System (AIS) as a competitive alternative to the typical machine learning techniques. The proposed algorithm's main function is to modify its dimensions and adjust the values of its parameters during the training or mutation process, with no complex operations and minimal parameter tuning. This analytical tool has been applied to the qualitative analysis of chocolate bars, classifying varieties in single and multi-label scenarios. The classification results demonstrate promising results in accurately distinguishing individual chocolate types, obtaining an average accuracy of 97%. Subsequently, multi-label classification (several labels associated with a single chocolate) was executed based on shared ingredient content, achieving an average accuracy of 94%. The evaluation and validation of the proposed AIS were also verified; the obtained performance metrics were compared with those of typical classification techniques, k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Satisfactory results show the competitive performance of AIS in single-label classification tasks compared to named techniques. Promisingly, a novel method for multi-label learning in e-noses is obtained to accurately identify different types of chocolates, outperforming methods like KNN and SVM. These results demonstrate the AIS potential as an effective classification method for e-nose applications.