Constraint nearest neighbor for instance reduction

In instance-based machine learning, algorithms often suffer from prohibitive computational costs and storage space. To overcome such problems, various instance reduction techniques have been developed to remove noises and/or redundant instances. Condensation approach is the most frequently used method, and it aims to remove the instances far away from the decision surface. Edition method is another popular one, and it removes noises to improve the classification accuracy. Drawbacks of these existing techniques include parameter dependency and relatively low accuracy and reduction rate. To solve these drawbacks, the constraint nearest neighbor-based instance reduction (CNNIR) algorithm is proposed in this paper. We firstly introduce the concept of natural neighbor and apply it into instance reduction to eliminate noises and search core instances. Then, we define a constraint nearest neighbor chain which only consists of three instances. It is used to select border instances which can construct a rough decision boundary. After that, a specific strategy is given to reduce the border set. Finally, reduced set is obtained by merging border and core instances. Experimental results show that compared with existing algorithms, the proposed algorithm effectively reduces the number of instances and achieves higher classification accuracy. Moreover, it does not require any user-defined parameters.