Application of Machine Learning Techniques for Real-time Classification of Sensor Array Data

There is a significant need to identify approaches for classifying chemical sensor array data with high success rates that would enhance sensor detection capabilities. The present study attempts to fill this need by investigating six machine learning methods to classify a dataset collected using a chemical sensor array: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Classification and Regression Trees (CART), Random Forest (RF), Naïve Bayes Classifier (NB), and Principal Component Regression (PCR). A total of 10 predictors that are associated with the response from 10 sensor channels are used to train and test the classifiers. A training dataset of 4 classes containing 136 samples is used to build the classifiers, and a dataset of 4 classes with 56 samples is used for testing. The results generated with the six different methods are compared and discussed. The RF, CART, and KNN are found to have success rates greater than 90%, and to outperform the other methods.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year

2009

Author dc:creator

• Li, Sichu

Contributors dc:contributor

• Zhu, Dongxiao
• Summa, Christopher M.
• Taylor, Christopher M.

• KNN
• SVM
• CART
• Random Forest
• Naïve Bayes
• PCR
• machine learning
• classification
• sensor array

Repository record dc:identifier

https://scholarworks.uno.edu/td/913

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-1894