School of Continuing Education, Shanghai Jiao Tong University, Shanghai 200240, China ; Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, China.
Department of Computer Science & Engineering, Shanghai Jiao Tong University, Shanghai 200240, China ; Center of Traditional Chinese Medicine Information Science and Technology, Shanghai University of TCM, Shanghai 201203, China.
Evid Based Complement Alternat Med. 2013;2013:493626. doi: 10.1155/2013/493626. Epub 2013 Dec 23.
Zheng classification is a very important step in the diagnosis of traditional Chinese medicine (TCM). In clinical practice of TCM, feature values are often missing and incomplete cases. The performance of Zheng classification is strictly related to rates of missing feature values. Based on the pattern of the missing feature values, a new approach named local-validity is proposed to classify zheng classification with missing feature values. Firstly, the maximum submatrix for the given dataset is constructed and local-validity method finds subsets of cases for which all of the feature values are available. To reduce the computational scale and improve the classification accuracy, the method clusters subsets with similar patterns to form local-validity subsets. Finally, the proposed method trains a classifier for each local-validity subset and combines the outputs of individual classifiers to diagnose zheng classification. The proposed method is applied to the real liver cirrhosis dataset and three public datasets. Experimental results show that classification performance of local-validity method is superior to the widely used methods under missing feature values.
郑分类是中医诊断的一个非常重要的步骤。在中医的临床实践中，特征值往往是缺失和不完整的情况。郑分类的性能与缺失特征值的比率严格相关。基于缺失特征值的模式，提出了一种新的方法，称为局部有效性，用于对缺失特征值的郑分类进行分类。首先，为给定数据集构建最大子矩阵，局部有效性方法找到所有特征值都可用的情况的子集。为了降低计算规模和提高分类精度，该方法将具有相似模式的子集聚类为局部有效性子集。最后，为每个局部有效性子集训练一个分类器，并结合各个分类器的输出来诊断郑分类。将所提出的方法应用于真实的肝硬化数据集和三个公共数据集。实验结果表明，在缺失特征值的情况下，局部有效性方法的分类性能优于广泛使用的方法。
