- 期刊
- OpenAccess
Extensions of k-Nearest Neighbor Algorithm
並列摘要
The aim of this study is to review various extensions of the nearest neighbor algorithm and discuss their approach along with limitations of the method. In nonparametric classification, no prior information is required for predicting the class label. k-Nearest Neighbor is the simplest and well known algorithm used in data mining The various extensions of k-nearest neighbor algorithm which have been studied are weighted nearest neighbor, feature selection methods, fuzzy nearest neighbor, genetic algorithm based classifiers and nearest neighbor algorithm using ensembling techniques.
延伸閱讀
- 古祐嘉（2009）。Adaptive K-Nearest Neighbor Algorithm〔碩士論文，元智大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0009-1106200913100600
- 凌鈺城（2007）。Reverse Nearest Neighbor Search in Metric Spaces with Adjustable Distance Functions〔碩士論文，國立清華大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0016-1411200715141930
- Zhan, J., Chang, L. W., & Matwin, S. (2005). Privacy Preserving K-nearest Neighbor Classification. International Journal of Network Security, 1(1), 46-51. https://doi.org/10.6633/IJNS.200507.1(1).07
- Gu, Y., Yu, G., & Yu, X. N. (2014). An Efficient Method for k Nearest Neighbor Searching in Obstructed Spatial Databases. Journal of Information Science and Engineering, 30(5), 1569-1583. https://doi.org/10.6688/JISE.2014.30.5.15
- Barigou, F. (2016). IMPROVING K-NEAREST NEIGHBOR EFFICIENCY FOR TEXT CATEGORIZATION. Neural Network World, 26(1), 45-65. https://doi.org/10.14311/NNW.2016.26.003