In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for each unseen instance. The widely known binary relevance method (BR) for multi-label classification considers each label as an independent binary problem. It is ignored in the literature due to inadequacy of not considering label correlations. In this paper, we present our double layer based classifier chains method (DCC), which overcomes disadvantages of BR and inherits the benefit of classifier chain method (CC). This algorithm decomposes the multi-label classification problem into two classification processes 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 expanded by all prior binary relevance predictions in the chain. This chaining allows DCC to take into account correlations in the label space. We also extend this approach further in an ensemble framework. An extensive evaluation covers a broad range of multi-label datasets with a variety of evaluation measures specifically designed for multi-label classification. Experiments on benchmark datasets validate the effectiveness of proposed approach comparing with state-of-art methods in terms of average ranking.