Multi-label Logistic Regression Using Relative Density Ratio

Abstract

Multi-label classification is a supervised learning problem where multiple labels may be assigned to each instance. The main baseline for multi-label classification is binary relevance method, which is estimate the binary classification model for each label. In binary classification, there are cases where poor results are data when the class is imbalance. In this paper, we propose a multi-label classification model used relative density ratio. In this model, we used relative F-measure by relative density ratio for weight of error function to solve the class imbalance problem.