High-Level K-Nearest Neighbors (HLKNN): A Supervised Machine Learning Model for Classification Analysis

被引:29
作者
Kiyak, Elife Ozturk
Ghasemkhani, Bita [1 ]
Birant, Derya [2 ]
机构
[1] Dokuz Eylul Univ, Grad Sch Nat & Appl Sci, TR-35160 Izmir, Turkiye
[2] Dokuz Eylul Univ, Dept Comp Engn, TR-35390 Izmir, Turkiye
关键词
machine learning; k-nearest neighbors; classification; supervised learning; artificial intelligence; CLASSIFIERS;
D O I
10.3390/electronics12183828
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The k-nearest neighbors (KNN) algorithm has been widely used for classification analysis in machine learning. However, it suffers from noise samples that reduce its classification ability and therefore prediction accuracy. This article introduces the high-level k-nearest neighbors (HLKNN) method, a new technique for enhancing the k-nearest neighbors algorithm, which can effectively address the noise problem and contribute to improving the classification performance of KNN. Instead of only considering k neighbors of a given query instance, it also takes into account the neighbors of these neighbors. Experiments were conducted on 32 well-known popular datasets. The results showed that the proposed HLKNN method outperformed the standard KNN method with average accuracy values of 81.01% and 79.76%, respectively. In addition, the experiments demonstrated the superiority of HLKNN over previous KNN variants in terms of the accuracy metric in various datasets.
引用
收藏
页数:20
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共 41 条
[1]   INSTANCE-BASED LEARNING ALGORITHMS [J].
AHA, DW ;
KIBLER, D ;
ALBERT, MK .
MACHINE LEARNING, 1991, 6 (01) :37-66
[2]  
Ahmad S.R., 2017, Journal of Telecommunication, Electronic and Computer Engineering, V9, P165, DOI DOI 10.1063/1.5005351
[3]   Improving the k-nearest neighbour rule: using geometrical neighbourhoods and manifold-based metrics [J].
Altincay, Hakan .
EXPERT SYSTEMS, 2011, 28 (04) :391-406
[4]   Concrete Strength Prediction Using Machine Learning Methods CatBoost, k-Nearest Neighbors, Support Vector Regression [J].
Beskopylny, Alexey N. ;
Stel'makh, Sergey A. ;
Shcherban', Evgenii M. ;
Mailyan, Levon R. ;
Meskhi, Besarion ;
Razveeva, Irina ;
Chernil'nik, Andrei ;
Beskopylny, Nikita .
APPLIED SCIENCES-BASEL, 2022, 12 (21)
[5]   Confidence of a k-Nearest Neighbors Python']Python Algorithm for the 3D Visualization of Sedimentary Porous Media [J].
Bullejos, Manuel ;
Cabezas, David ;
Martin-Martin, Manuel ;
Alcala, Francisco Javier .
JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2023, 11 (01)
[6]   A K-Nearest Neighbors Algorithm in Python']Python for Visualizing the 3D Stratigraphic Architecture of the Llobregat River Delta in NE Spain [J].
Bullejos, Manuel ;
Cabezas, David ;
Martin-Martin, Manuel ;
Alcala, Francisco Javier .
JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2022, 10 (07)
[7]  
Cha Gi-Wook, 2023, Int J Environ Res Public Health, V20, DOI [10.3390/ijerph20043159, 10.3390/ijerph20043159]
[8]   Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision [J].
Corso, Marcelo Picolotto ;
Perez, Fabio Luis ;
Stefenon, Stefano Frizzo ;
Yow, Kin-Choong ;
Garcia Ovejero, Raul ;
Quietinho Leithardt, Valderi Reis .
COMPUTERS, 2021, 10 (09)
[9]   Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security [J].
Derhab, Abdelouahid ;
Guerroumi, Mohamed ;
Gumaei, Abdu ;
Maglaras, Leandros ;
Ferrag, Mohamed Amine ;
Mukherjee, Mithun ;
Khan, Farrukh Aslam .
SENSORS, 2019, 19 (14)
[10]   Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting [J].
Fan, Guo-Feng ;
Guo, Yan-Hui ;
Zheng, Jia-Mei ;
Hong, Wei-Chiang .
ENERGIES, 2019, 12 (05)