An Improved Binary Relevance Algorithm for Multi-Label Classification

Abstract

Multi-label classification (MLC) is a machine learning task aiming to predict multiple labels for a given instance. The widely known binary relevance (BR) learns one classifier for each label without considering the correlation among labels. In this paper, an improved binary relevance algorithm (IBRAM) is proposed. This algorithm is derived form binary relevance method. It sets two layers to decompose the multi-label classification problem into L independent binary classification problems respectively. In the first layer, binary classifier is built one for each label. In the second layer, the label information from the first layer is fully used to help to generate final predicting by consider the correlation among labels. Experiments on benchmark datasets validate the effectiveness of proposed approach against other well-established methods.