Soft class decision for nursing-care text classification using a k-nearest neighbor based system

In the aging society such as Japan, it is very important to improve the quality of nursing-care for keeping our quality of life. Our final goal is to develop a computer aided evaluation system to improve the quality of nursing-care. For evaluating the quality of actual nursing, we have been collecting texts that are written by nurses using our Web based system. In our previous works, a SVM based classification system has been developed to classify such nursing-care texts, and a dependency relation based feature vector definition has also been proposed. The training data are pre-classified texts by a few nursing-care experts. Some texts in the training data are similar but classified into different classes. To classify the nursing-care texts with high accuracy, we need to tackle such ambiguous class labels in the training data. In this paper, we propose a k-nearest neighbor based classification system which can classify into classes with certainty grade.