A High-performance Classifier from K-dimensional Tree-based Dual-kNN
Swe Swe Aung, Itaru Nagayama, Shiro Tamaki · IEIE Transactions on Smart Processing and Computing · 2018
The k-nearest neighbors (kNN) method is highly effective in many application areas. Conceptually, its other good properties are simplicity and ease of understanding. However, according to measurements of the performance of algorithms based on three considerations (simplicity, processing time, and prediction power), the classic kNN algorithm lacks high-speed computation as well as maintenance of high accuracy for different values of k. The k-nearest neighbors algorithm is still influenced by varying k values and high variance in the training data set. Prediction accuracy diminishes when k approaches larger values. To overcome these issues, this paper introduces a k-dimensional (kd)-tree–based dual-kNN approach that concentrates on two properties to maintain classification accuracy at different k values and that also upgrades processing time performance. By conducting experiments on real data sets and comparing this algorithm with two other algorithms (dual-kNN and classic kNN), it was experimentally confirmed that the kdtree–based dual-kNN is a more effective and robust approach for classification than pure dual-kNN and classic kNN.