Department of Industrial Engineering, Engineering College, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea.
Comput Biol Med. 2012 Dec;42(12):1157-64. doi: 10.1016/j.compbiomed.2012.10.001. Epub 2012 Nov 14.
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<0.001). The reduction of classification time as well as the improvement of overall accuracy demonstrates promise for the proposed classification method to be adopted in various real-time and on-line image-based clinical applications.
为了提高计算机辅助量化区分弥漫性间质性肺病的时间和准确性，我们引入了一种层次支持向量机，该方法通过在层次结构中的每个节点训练二进制分类器来选择类别，从而允许每个分类器使用特定于类的准最优特征集。此外，应用了计算成本敏感的组特征选择准则与顺序前向选择相结合，以便为加速分类时间获得有用且计算成本低的准最优特征集。与具有顺序前向选择的一对一和一对多支持向量机方法相比，分类时间最多减少了 57%，整体准确性显著提高（配对 t 检验，p<0.001）。分类时间的减少和整体准确性的提高表明，所提出的分类方法有望应用于各种实时和在线基于图像的临床应用中。
