Improving Multi-label Medical Text Classification by Feature Selection
Kinga Glinka, Rafał Woźniak, Danuta Zakrzewska · 2017
Multi-label text classification plays a significant role in information retrieval area. The effectiveness of the techniques is especially important in the case of medical documents. In the paper, application of feature selection methods for improving multi-label medical text classification is discussed. We examine combining problem transformation methods with different approaches to feature selection techniques including the hybrid ones. We check the performance of the considered methods by experiments conducted on the dataset of free medical text reports. There are considered cases of different number of labels and data instances. The obtained results are evaluated and compared by using two metrics: Classification Accuracy and Hamming Loss.