KNN parameter selection via meta learning

In this study, the K Nearest Neighbor's parameter k is predicted by system. Meta learning method is used for prediction. Getting training set with meta-features, 200 data sets were used. For each of them, 16 meta-features were extracted. The K Nearest Neighbour algorithm was applied each of them with most common 6 k values the best one is selected. With this training set it is possible to predict a new data set's best k value. In 200 data sets the most common k value which has best performance is 1. 4 methods are applied on the model. Generally all methods used same features and some meta-features are never used.