An Approach on Feature Selection of Cascaded Support Vector Machines with Particle Swarm Optimization Algorithm

Rui-hu, Wang; Hu, Zhangping; Chen, Liang; Xiong, Jing

doi:10.1007/978-3-642-27296-7_104

Cited by 1 publication

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“…For example, a higher value of gamma leads the over-fitting problem because it tries to fit exactly each data point in the training set. Whereas, the cost is used to control the trade-off between smooth decision boundary and classifying the data points in the training set correctly [25,26].…”

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confidence: 99%

Design of Electronic Nose System Using Gas Chromatography Principle and Surface Acoustic Wave Sensor

et al. 2018

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Most gases are odorless, colorless and also hazard to be sensed by the human olfactory system. Hence, an electronic nose system is required for the gas classification process. This study presents the design of electronic nose system using a combination of Gas Chromatography Column and a Surface Acoustic Wave (SAW). The Gas Chromatography Column is a technique based on the compound partition at a certain temperature. Whereas, the SAW sensor works based on the resonant frequency change. In this study, gas samples including methanol, acetonitrile, and benzene are used for system performance measurement. Each gas sample generates a specific acoustic signal data in the form of a frequency change recorded by the SAW sensor. Then, the acoustic signal data is analyzed to obtain the acoustic features, i.e. the peak amplitude, the negative slope, the positive slope, and the length. The Support Vector Machine (SVM) method using the acoustic feature as its input parameters are applied to classify the gas sample. Radial Basis Function is used to build the optimal hyperplane model which devided into two processes i.e., the training process and the external validation process. According to the result performance, the training process has the accuracy of 98.7% and the external validation process has the accuracy of 93.3%. Our electronic nose system has the average sensitivity of 51.43 Hz/mL to sense the gas samples.

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confidence: 99%