Decision tree approach for classification and dimensionality reduction of electronic nose data

作者：Cho Jung Hwan^*; Kurup Pradeep U

来源：Sensors and Actuators B: Chemical , 2011, 160(1): 542-548.

DOI：10.1016/j.snb.2011.08.027

• Decision tree
• C4.5
• CART
• Classification
• Dimensionality reduction
• Feature selection
• Sensor parameter
• Dynamic sensor response

This paper presents a decision tree approach using two different tree models, C4.5 and CART, for use in the classification and dimensionality reduction of electronic nose (EN) data. The decision tree is a tree structure consisting of internal and terminal nodes which process the data to ultimately yield a classification. The decision tree is proficient at both maintaining the role of dimensionality reduction and at organizing optimally sized classification trees, and therefore it could be a promising approach to analyze EN data. In the experiments conducted, six sensor response parameters were extracted from the dynamic sensor responses of each of the four metal oxide gas sensors. The six parameters observed were the rising time (T(r)), falling time (T(f)), total response time (T(t)), normalized peak voltage change (y(p,n)), normalized curve integral (C(I)), and triangle area (T(A)). One sensor parameter from each metal oxide sensor was used for the classification trees, and the best classification accuracy of 97.78% was achieved by CART using the CI parameter. However, the accuracy of CART was improved using all of the sensor parameters as inputs to the classification tree. The improved results of CART, having an accuracy of 98.89%, was comparable to that of two popular classifiers, the multilayer perceptron (MLP) neural network and the fuzzy ARTMAP network (accuracy of 98.89%, and 100%, respectively). Furthermore, as a dimensionality reduction method the decision tree has shown a better discrimination accuracy of 100% for the MLP classifier and 98.89% for the fuzzy ARTMAP classifier as compared to those achieved with principle component analysis (PCA) giving 81.11% and 97.78%, and a variable selection method giving 92.22% and 93.33% (for the same MLP and fuzzy ARTMAP classifiers). Therefore, a decision tree could be a promising technique for a pattern recognition system for EN data in terms of two functions; as classifier which is an optimally organized classification tree, and as dimensionality reduction method for other pattern recognition techniques.

• 出版日期2011-12-15