Cost-Sensitive Classification on Pathogen Species of Bacterial Meningitis by Surface Enhanced Raman Scattering.
Journal
IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011, Atlanta, GA, USA, November 12-15, , 2011
Pages
390-393
Date Issued
2011
Author(s)
Jan, Te-Kang
Lin, Hsuan-Tien
Chen, Hsin-Pai
Chern, Tsung-Chen
Huang, Chung-Yueh
Wen, Bing-Cheng
Chung, Chia-Wen
Li, Yung-Jui
Chuang, Ya-Ching
Li, Li-Li
Chan, Yu-Jiun
Wang, Juen-Kai
Wang, Yuh-Lin
Lin, Chi-Hung
Abstract
We propose a pathogen-classification system using the Surface-Enhanced Raman Scattering (SERS) platform. The system differentiates the pathogens based on their SERS spectra, which are believed to be related to the surface chemical components. The specialty of the system is to not only consider the usual classification accuracy, but also pay attention to the different types of costs during misclassification. For instance, due to the effectiveness of treatments, the cost of classifying a Gram-positive bacterium as another Gram-positive one should be lower than the cost of classifying a Gram-positive bacterium as a Gram-negative one. We express the task as the cost-sensitive classification problem, and take state-of-the-art cost-sensitive classification algorithms from the machine learning community to conquer the task. Our experimental study validates the usefulness of those algorithms on building the system.
Type
conference paper