Feature selection is crucial for dimension reduction. Dozens of approaches employ the area under ROC curve, i.e., AUC, to evaluate features, and have shown their attractiveness in finding discriminative targets. However, feature complementarity for jointly discriminating classes is generally improperly handled by these approaches. In a recent approach to deal with such issues, feature complementarity was evaluated by computing the difference between the neighbors of each instance in different feature dimensions. This local-learning based approach introduces a distinctive way to determine how a feature is complementarily discriminative given another. Nevertheless, neighbor information is usually sensitive to noises. Furthermore, evaluating merely one-side information of nearest misses will definitely neglect the impacts of nearest hits on feature complementarity. In this paper, we propose to integrate all-side local-learning based complementarity into an AUC-based approach, dubbed ANNC, to evaluate pairwise features by scrutinizing their comprehensive misclassification information in terms of both k-nearest misses and k-nearest hits. This strategy contributes to capture complementary features that collaborate with each other to achieve remarkable recognition performance. Extensive experiments on openly available benchmarks demonstrate the effectiveness of the new approach under various metrics.