Label ranking aims to map instances to an order over a predefined set of labels. It is ideal that the label ranking model is trained by directly maximizing performance measures on training data. However, existing studies on label ranking models mainly based on the minimization of classification errors or rank losses. To fill in this gap in label ranking, in this paper a novel label ranking model is learned by minimizing a loss function directly defined on the performance measures. The proposed algorithm, referred to as BoostLR, employs a boosting framework and utilizes the rank aggregation technique to construct weak label rankers. Experimental results reveal the initial success of BoostLR.