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however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine.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  - 
TY  - JOUR
A1  - Shamshirband, Shahaboddin
A1  - Babanezhad, Meisam
A1  - Mosavi, Amir
A1  - Nabipour, Narjes
A1  - Hajnal, Eva
A1  - Nadai, Laszlo
A1  - Chau, Kwok-Wing
T1  - Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants
JF  - Engineering Applications of Computational Fluid Mechanics
N2  - A novel combination of the ant colony optimization algorithm (ACO)and computational fluid dynamics (CFD) data is proposed for modeling the multiphase chemical reactors. The proposed intelligent model presents a probabilistic computational strategy for predicting various levels of three-dimensional bubble column reactor (BCR) flow. The results prove an enhanced communication between ant colony prediction and CFD data in different sections of the BCR.
KW  - Maschinelles Lernen
KW  - Machine learning
KW  - Bubble column reactor
KW  - ant colony optimization algorithm (ACO)
KW  - flow pattern
KW  - computational fluid dynamics (CFD)
KW  - big data
KW  - OA-Publikationsfonds2020
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200227-41013
UR  - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1715842
VL  - 2020
IS  - volume 14, issue 1
SP  - 367
EP  - 378
PB  - Taylor & Francis
ER  -