How to assign weights on samples is an important subject in weighted least squares support vector machine (WLS-SVM) for regression problems, which largely influences the robustness of the WLS-SVM. Based on the local outlier factor (LOF), a useful factor for detecting outlier in knowledge discovery, we propose a local-density-ratio (LDR) based weightsetting algorithm for WLS-SVR in this paper. In the proposed algorithm, weights are assigned to the samples according to their neighborhood density ratios. In order to simplify the parameter selection, a single parameter strategy is introduced, which avoids choosing two thresholds in other heuristic weightsetting strategies. Numerical experiments show that the proposed algorithm is able to distinguish most of the noises and produces robust estimator.