Abstract— Hybrid k -Nearest Neighbor Classifier. Conventional k-nearest neighbor < Final Year Projects 2016 > classification approaches have several limitations when dealing with some problems caused by the special datasets, such as the sparse problem, the imbalance problem, and the noise problem. We first perform a brief survey on the recent progress of the KNN classification approaches. . Then, the hybrid KNN (HBKNN) classification approach, which takes into account the local and global information of the query sample, is designed to address the problems raised from the special datasets. In the following, the random subspace ensemble framework based on HBKNN (RS-HBKNN) classifier is proposed to perform classification on the datasets with noisy attributes in the high-dimensional space.