Distance mean-square loss function for ordinal text classification of emergency service response codes in disaster management

Abstract

The National Fire Agency (NFA) and National Police Agency (NPA) have defined risk levels based on the severity of disasters. Risk-level data possess the characteristics of ordinal data such as NPA's Emergency Service Response Code (ESRC) data, which are classified based on their magnitudes (from C0 to C4). In this study, we propose a distance mean-square (DiMS) loss function to improve the accuracy of ordinal data classification. The DiMS loss function calculates loss values based on the distances between the predicted and true labels: value distances (commonly used in regression analysis for magnitude data) and probability distances (typically used in classification analysis). Therefore, the DiMS loss function contributes to improved accuracy when classifying ordinal data, such as ESRC. In addition, using the DiMS loss function, we achieved state-of-the-art performance in classifying the SST-5 data, which is a representative ordinal dataset. The DiMS loss function for ordinal classification enabled accurate risk recognition. Thus, accurate risk recognition using the DiMS loss function enhances disaster response.