SV-kNNC: An Algorithm for Improving the Efficiency of k-Nearest Neighbor

Abstract

This paper proposes SV-kNNC, a new algorithm for k-Nearest Neighbor (kNN). This algorithm consists of three steps. First, Support Vector Machines (SVMs) are applied to select some important training data. Then, k-mean clustering is used to assign the weight to each training instance. Finally, unseen examples are classified by kNN. Fourteen datasets from the UCI repository were used to evaluate the performance of this algorithm. SV-kNNC is compared with conventional kNN and kNN with two instance reduction techniques: CNN and ENN. The results show that our algorithm provides the best performance, both predictive accuracy and classification time.

Towards Effective Bug Triage with Software Data Reduction Techniques

“…Instance selection is a technique to reduce the number of instances by removing noisy and redundant instances [48], [11]. An instance selection algorithm can provide a reduced data set by removing non-representative instances [65], [38].…”

Survey on Bug Triage with Software Data Reduction Techniques

“…Srisawat, T. Phienthrakul, and B. Kijsirikul, [8], paper proposed SV-kNNC approach for data reduction to enhance performance of kNN. Proposed algorithm is three fold approaches, first support vector machines (SVMs) are applied to select some important training data then weights are allocate to each training instance based on k-mean clustering and finally classify the query instances by kNN classification process.…”

“…SV-kNNC: An algorithm for improving the efficiency of knearest neighbor [8] Paper proposed SV-kNNC approach for data reduction to enhance performance of kNN.…”

Selecting Representative Data Sets

“…Some of them are based on evolutionary algorithms (EA) [38,64,84,91], other methods use the support vector machine (SVM) [9,17,61,62] or tabu search (TS) [18,42,103].…”