Hedged predictions for traditional Chinese chronic gastritis diagnosis with confidence machine

Authors:
Huazhen Wang;Chengde Lin;Fan Yang;Xueqin Hu
Affiliations:
College of Computer Science and Technology, Huaqiao University, Xiamen 361005, PR China and School of Information Science and Technology, Xiamen University, Xiamen 361005, PR China;School of Information Science and Technology, Xiamen University, Xiamen 361005, PR China;School of Information Science and Technology, Xiamen University, Xiamen 361005, PR China;College of Basic Medical Science, Shanghai University of Chinese Medicine, Shanghai 201203, PR China
Venue:
Computers in Biology and Medicine
Year:
2009

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Abstract

Most classifiers output predictions for new instances without indicating how reliable they could be. Transductive confidence machine (TCM) is a novel framework that provides hedged prediction coupled with valid confidence. Many popular machine learning algorithms can be transformed into the framework of TCM, and therefore be used for producing hedged predictions. This paper incorporates random forest (RF) to propose a method named TCM-RF for classification of chronic gastritis data. Our method benefits from TCM-RF's high performance when features are noisy, highly correlated and of mixed types. The experimental results show that TCM-RF produces informative as well as effective predictions.