Comparisons of parametric and non-parametric classification rules for e-nose and e-tongue

• Main Authors: Mahat, Nor Idayu, Zakaria, Ammar, Md Shakaff, Ali Yeon
• Format: Conference or Workshop Item
• Language: English
• Published: 2015
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://repo.uum.edu.my/16796/1/2.pdf; http://repo.uum.edu.my/16796/; http://doi.org/10.1063/1.4937017

This paper evaluates the performance of parametric and non-parametric classification rules in sensor technology.The growing of sensor technologies, e-nose and e-tongue, has urged engineers to equip themselves with the utmost recent and advanced statistical approaches.As data collected from e-nose and e-tongue face some complexities, often data pre-processing and transformation are performed prior to the classification.This paper discusses the comparisons made on some known parametric and non-parametric classification rules in the application for classifying data of e-nose and e-tongue.The comparisons which based on leave-one-out accuracy, sensitivity and specificity shows that non-parametric approaches especially k-nearest neighbour does not much distorted with changes of distribution, but Naïve Bayes is greatly influenced by the structure of the data.