TY  - JOUR
A1  - Saadatfar, Hamid
A1  - Khosravi, Samiyeh
A1  - Hassannataj Joloudari, Javad
A1  - Mosavi, Amir
A1  - Shamshirband, Shahaboddin
T1  - A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning
JF  - Mathematics
N2  - 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however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine.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.
KW  - Maschinelles Lernen
KW  - Machine learning
KW  - K-nearest neighbors
KW  - KNN
KW  - classifier
KW  - big data
KW  - clustering
KW  - cluster shape
KW  - cluster density
KW  - classification
KW  - reinforcement learning
KW  - data science
KW  - computation
KW  - artificial intelligence
KW  - OA-Publikationsfonds2020
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200225-40996
UR  - https://www.mdpi.com/2227-7390/8/2/286
VL  - 2020
IS  - volume 8, issue 2, article 286
PB  - MDPI
ER  -