An Improved Multi-label Classifier Chain Derived from Binary Relevance

In multi-label classification, each training example is associated with a set of labels and the task for classification is to predict the proper label set for each unseen instance. Recently, multi-label classification methods mainly focus on exploiting the label correlations to improve the accuracy of individual multi-label learner. In this paper, an improved method derived from binary relevance named double layer classifier chaining (DCC) is proposed. This algorithm decomposes the multi-label classification problem into two stages classification process to generate classifier chain. Each classifier in the chain is responsible for learning and predicting the binary association of the label given the attribute space, augmented by all prior binary relevance predictions in the chain. This chaining allows DCC to take into account correlations in the label space. Experiments on benchmark datasets validate the effectiveness of proposed approach comparing with other well-established methods.