An Optimized Hybrid Techniques of Training set reduction for Performance Improvement of k- Nearest Neighbour Classifier to apply it on Agricultural Soil health card dataset

Abstract— In non-parametric algorithms such as k-nearest neighbour the fundamental predicaments are the larger storage and computational requirements. Moreover, the effectiveness of classification task affected significantly due to uneven distribution of training data. To overcome the drawbacks of lazy learner like k-nearest neighbour classifier, the scope of training set reduction by editing and condensing the training set is explored in this research work. Additionally, the reduction of training set is carried out by hybrid techniques of training set reduction namely TSR-FkNN (Elbow method) and TRS-FkNN (Silhouette value) in optimized way to achieve improvement of classification performance.