Fast and efficient lung disease classification using hierarchical one-against-all support vector machine and cost-sensitive feature selection

Authors:
Yongjun Chang;Namkug Kim;Youngjoo Lee;Jonghyuck Lim;Joon Beom Seo;Young Kyung Lee
Affiliations:
Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 388-1 Pungnap2-dong, Songpa-gu, Seoul 138-736, Republic of Korea;Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 388-1 Pungnap2-dong, Songpa-gu, Seoul 138-736, Republic of Korea;Department of Industrial Engineering, Engineering College, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea;Department of Industrial Engineering, Engineering College, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea;Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 388-1 Pungnap2-dong, Songpa-gu, Seoul 138-736, Republic of Korea;East-West Neo Medical Center, Kyunghee University, 149 Sangil-dong, Gangdong-gu, Seoul 134-727, Republic of Korea
Venue:
Computers in Biology and Medicine
Year:
2012

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Abstract

To improve time and accuracy in differentiating diffuse interstitial lung disease for computer-aided quantification, we introduce a hierarchical support vector machine which selects a class by training a binary classifier at each node in a hierarchy, thus allowing each classifier to use a class-specific quasi-optimal feature set. In addition, the computational cost-sensitive group-feature selection criterion combined with the sequential forward selection is applied in order to obtain a useful and computationally inexpensive quasi-optimal feature set for the purpose of accelerating the classification time. The classification time was reduced by up to 57% and the overall accuracy was significantly improved in comparison with the one-against-all and one-against-one support vector machine methods with sequential forward selection (paired t-test, p