대용량 자료에 대한 밀도 적응 격자 기반의 k-NN 회귀 모형

• Published in: 品質經營學會誌 Vol. 49; no. 2; pp. 201 - 211
• Main Authors: 유의기, Yiqi Liu, 정욱, Jung Uk
• Format: Journal Article
• Language: Korean
• Published: 한국품질경영학회 30.06.2021
• Subjects: Computation Time; Density; Grid; k-nearest Neighbor; Regression; 학제간연구; Regression; k-nearest Neighbor; Density; Computation Time; Grid
• ISSN: 1229-1889; 2287-9005
• DOI: 10.7469/JKSQM.2021.49.2.201

Purpose: This paper proposes a density adaptive grid algorithm for the k-NN regression model to reduce the computation time for large datasets without significant prediction accuracy loss. Methods: The proposed method utilizes the concept of the grid with centroid to reduce the number of reference data points so that the required computation time is much reduced. Since the grid generation process in this paper is based on quantiles of original variables, the proposed method can fully reflect the density information of the original reference data set. Results: Using five real-life datasets, the proposed k-NN regression model is compared with the original k-NN regression model. The results show that the proposed density adaptive grid-based k-NN regression model is superior to the original k-NN regression in terms of data reduction ratio and time efficiency ratio, and provides a similar prediction error if the appropriate number of grids is selected. Conclusion: The proposed density adaptive grid algorithm for the k-NN regression model is a simple and effective model which can help avoid a large loss of prediction accuracy with faster execution speed and fewer memory requirements during the testing phase.