A new weak-ranker construction method based on Data Envelopment Analysis technique is presented. Each weak ranker represents a feature subset drawn from the complete feature space. Two linear programming models are formulated, both of which treat the documents to be ranked as the decision making units. By solving the models, we construct a pool of weak-ranker candidates from the optimal weight vectors, and then develop DEARank algorithm based on Boosting technique. We conduct extensive experiments on LETOR 3.0 and LETOR 4.0 collections, with twelve well-known algorithms as the baselines. The experimental results indicate that DEARank is a competitive learning to rank algorithm.