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A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning

  • The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a largeThe K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods.show moreshow less

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    Gefördert durch das Programm Open Access Publizieren der DFG und den Publikationsfonds der Bauhaus-Universität Weimar.

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Metadaten
Document Type:Article
Author: Hamid SaadatfarORCiD, Samiyeh Khosravi, Javad Hassannataj JoloudariORCiD, Amir MosaviORCiD, Shahaboddin ShamshirbandORCiD
DOI (Cite-Link):https://doi.org/10.3390/math8020286Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200225-40996Cite-Link
URL:https://www.mdpi.com/2227-7390/8/2/286
Parent Title (German):Mathematics
Publisher:MDPI
Language:English
Date of Publication (online):2020/02/21
Date of first Publication:2020/02/20
Release Date:2020/02/25
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:volume 8, issue 2
Pagenumber:12
Tag:K-nearest neighbors; KNN; Machine learning; artificial intelligence; big data; classification; classifier; cluster density; cluster shape; clustering; computation; data science; reinforcement learning
GND Keyword:Maschinelles Lernen
Dewey Decimal Classification:500 Naturwissenschaften und Mathematik
BKL-Classification:54 Informatik
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2020
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)