Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific features. Label-specific features means that each class label is supposed to have its own characteristics and is determined by some specific features that are the most discriminative features for that label. LIFT employs clustering methods to discover the properties of data. More precisely, LIFT divides the training instances into positive and negative clusters for each label which respectively consist of the training examples with and without that label. It then selects representative centroids in the positive and negative instances of each label by k-means clustering and replaces the original features of a sample by the distances to these representatives. Constructing new features, the dimensionality of the new space reduces significantly. However, to construct these new features, the original features are needed. Therefore, the complexity of the process of multi-label classification does not diminish, in practice. In this paper, we make a modification on LIFT to reduce the computational burden of the classifier and improve or at least preserve the performance of it, as well. The experimental results show that the proposed algorithm has obtained these goals, simultaneously.