Dual-kNN for a Pattern Classification Approach
Swe Swe Aung, Itaru Nagayama, Shiro Tamaki · IEIE Transactions on Smart Processing and Computing · 2017
Classification is a process of discovering and categorizing objects from large data storage that have similar characteristics, properties, and patterns. One of the most widely used classification methods in machine learning is the k-nearest neighbors (k-NN) algorithm. The unique property of k-NN that appeals to researchers is its simplicity, so it can be applied successfully over a wide field. However, according to measurement of the performance of an algorithm based on three considerations (simplicity, processing time, and prediction power), the k-NN algorithm lacks highspeed computation and maintenance of high accuracy for different K values. In other words, k-NN is a heuristic classification approach. Besides, the prediction accuracy fades away whenever K approaches larger values. To overcome these issues, this paper presents a dual-kNN that concentrates on two properties to keep up the accuracy at different K values and upgrade processing time performance. By conducting experiments on real datasets and comparing this algorithm with k-NN, it was also confirmed that the new dual-kNN is an effective and robust approach to classification.