title: Pseudo optimization of e-nose data using region selection with feature feedback based on regularized linear discriminant analysis.
abstract: In this paper, we present a pseudo optimization method for electronic nose (e-nose) data using region selection with feature feedback based on regularized linear discriminant analysis (R-LDA) to enhance the performance and cost functions of an e-nose system. To implement cost- and performance-effective e-nose systems, the number of channels, sampling time and sensing time of the e-nose must be considered. We propose a method to select both important channels and an important time-horizon by analyzing e-nose sensor data. By extending previous feature feedback results, we obtain a two-dimensional discriminant information map consisting of channels and time units by reverse mapping the feature space to the data space based on R-LDA. The discriminant information map enables optimal channels and time units to be heuristically selected to improve the performance and cost functions. The efficacy of the proposed method is demonstrated experimentally for different volatile organic compounds. In particular, our method is both cost and performance effective for the real implementation of e-nose systems.
authors.affiliation: Electrical Engineering, Kookmin University, 861-1, Jeongeung-dong, Songbuk-gu, Seoul 136-702, Korea. gm1004@kookmin.ac.kr.
authors.forename: Gu-Min
authors.initials: GM
authors.lastname: Jeong
authors.affiliation: Electrical Engineering, Kookmin University, 861-1, Jeongeung-dong, Songbuk-gu, Seoul 136-702, Korea. nghiavp07@gmail.com.
authors.forename: Nguyen Trong
authors.initials: NT
authors.lastname: Nghia
authors.affiliation: Department of Computer Science and Engineering, Dankook University, 126, Jukjeon-dong, Suji-gu, Yongin-si, Gyeonggi-do 448-701, Korea. choisi@dankook.ac.kr.
authors.forename: Sang-Il
authors.initials: SI
authors.lastname: Choi
mesh_terms: D016002:Discriminant Analysis; D062609:Electronic Nose; D005246:Feedback; D025341:Principal Component Analysis; D015203:Reproducibility of Results