An Instance Selection Algorithm Based on Reverse Nearest Neighbor

Data reduction is to extract a subset from a dataset. The advantages of data reduction are decreasing the requirement of storage and increasing the efficiency of classification. Using the subset as training data is possible to maintain classification accuracy; sometimes, it can be further improved because of eliminating noises. The key is how to choose representative samples while ignoring noises at the same time. Many instance selection algorithms are based on nearest neighbor decision rule (NN). Some of these algorithms select samples based on two strategies, incremental and decremental. The first type of algorithms select some instances as samples and iteratively add instances which do not have the same class label with their nearest sample to the sample set. The second type of algorithms remove instances which do not have the same class label with their majority of kNN. However, we propose an algorithm based on Reverse Nearest Neighbor (RNN), called the Reverse Nearest Neighbor Reduction (RNNR). RNNR selects samples which can represent other instances in the same class. In addition, RNNR does not need to iteratively scan a dataset which takes much processing time. Experimental results show that RNNR achieves comparable accuracy and selects fewer samples than comparators.