earticle

영어

Predicting disaster classification on time is critical to mitigate the damage. Since identifying disaster types requires large amounts of data, and real-world data are often imbalanced, there are many recent works addressing data imbalance problems using generative models. However, if the process of generating text data based on disaster classes and severity is not handled improperly, the quality of the data can be degraded as well as the performance of classification predictions. In this paper, we propose a scheme for generating data with enhanced quality using text based on labels such as informational value of text and severity of disasters. Our experiment results verify the quality of data through the comparisons of prediction performance between various machine learning models.