Dependent nearest neighbor (dNN) method for improving the performance of categorical data classification

Classification analysis is a method used to predict classes or categories based on data class that has been determined previously. The k Nearest Neighbor (kNN) method is one of the most widely used methods in classification analysis due to the ease and simplicity of the algorithm. This strategy, however, is not without flaws; the determination of k, which is difficult to determine, and the determination based on the value of k results in data being classified into a particular class, even though it has a long distance. The Dependent Nearest Neighbor (dNN) method is a method that determines the nearest neighbor based on similarities and dependencies. In the dNN method, the closest selected neighbor is a sample in the Dependency Region (DR). DR is an area formed from the parameters of the radius and an angle. This study compares the performance generated by kNN and dNN using three datasets. Based on the analysis that has been done, the accuracy produced by the dNN method is greater than the kNN method.