title: Data refinement and channel selection for a portable e-nose system by the use of feature feedback.
abstract: We propose a data refinement and channel selection method for vapor classification in a portable e-nose system. For the robust e-nose system in a real environment, we propose to reduce the noise in the data measured by sensor arrays and distinguish the important part in the data by the use of feature feedback. Experimental results on different volatile organic compounds data show that the proposed data refinement method gives good clustering for different classes and improves the classification performance. Also, we design a new sensor array that consists only of the useful channels. For this purpose, each channel is evaluated by measuring its discriminative power based on the feature mask used in the data refinement. Through the experimental results, we show that the new sensor array improves both the classification rates and the efficiency in computation and data storage.
authors.affiliation: School of Electrical Engineering and Computer Science, Seoul National University 1, #047 San 56-1, Sillim-dong, Gwanak-gu, Seoul 151-744, Korea.
authors.forename: Sang-Il
authors.initials: SI
authors.lastname: Choi
authors.forename: Su-Hyun
authors.initials: SH
authors.lastname: Kim
authors.forename: Yoonseok
authors.initials: Y
authors.lastname: Yang
authors.forename: Gu-Min
authors.initials: GM
authors.lastname: Jeong
mesh_terms: D000465:Algorithms; D004583:Electrons; D005740:Gases / Q000032:analysis
chemical_list: D005740:Gases