In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax Cheonan Soonchunhyang University Hospital including Tuberculosis were used. To improve performance, preprocessing was performed Cent...

Abstract Model uncertainty has gained popularity in machine learning due to the overconfident predictions derived from standard neural networks which are not trustworthy. Recently, Monte-Carlo based adversarial attack (MC-AA) been proposed as a simple estimation method is powerful capturing data points that lie overlapping distribution of decision boundary. MC-AA produces uncertainties by perfo...

We develop the concept of ABC-Boost (Adaptive Base Class Boost) for multi-class classification and present ABC-MART, a concrete implementation of ABC-Boost. The original MART (Multiple Additive Regression Trees) algorithm has been very successful in large-scale applications. For binary classification, ABC-MART recovers MART. For multi-class classification, ABC-MART considerably improves MART, a...