Hedged predictions for traditional Chinese chronic gastritis diagnosis with confidence machine

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.