Title : ( A novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs )
Authors: Mohammad Beheshti Roui , Mariam Zomorodi-Moghadam , masoomeh sarvelayati , Moloud Abdar , Hamid Noori , Pawel Plawiak , Ryszard Tadeusiewicz , Xujuan Zhou , Abbas Khosravi , Saeid Nahavandi , U. Rajendra Acharya ,
Full TextAbstract
This paper proposes a new approach to produce classification rules based on evolutionary computation with novel crossover and mutation operators customized for execution on graphics processing unit (GPU). Also, a novel method is presented to define the fitness function, i.e. the function which measures quantitatively the accuracy of the rule. The proposed fitness function is benefited from parallelism due to the parallel execution of data instances. To this end, two novel concepts; coverage matrix and reduction vectors are used and an altered form of the reduction vector is compared with previous works. Our CUDA program performs operations on coverage matrix and reduction vector in parallel. Also these data structures are used for evaluation of fitness function and calculation of genetic operators in parallel. We proposed a vector called average coverage to handle crossover and mutation properly. Our proposed method obtained a maximum accuracy of 99.74% for Hepatitis C Virus (HCV) dataset, 95.73% for Poker dataset, and 100% for COVID-19 dataset. Our speedup is higher than 20% for HCV and COVID-19, and 50% for Poker, compared to using single core processors.
Keywords
Data mining Machine learning Rule discovery Genetic algorithm GPU programming Classification rules
@article{paperid:1088520,
author = {Beheshti Roui, Mohammad and Zomorodi-Moghadam, Mariam and Sarvelayati, Masoomeh and Moloud Abdar and Noori, Hamid and Pawel Plawiak and Ryszard Tadeusiewicz and Xujuan Zhou and Abbas Khosravi and Saeid Nahavandi and U. Rajendra Acharya},
title = {A novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs},
journal = {Knowledge-Based Systems},
year = {2021},
volume = {231},
number = {1},
month = {November},
issn = {0950-7051},
pages = {107419--107419},
numpages = {0},
keywords = {Data mining
Machine learning
Rule discovery
Genetic algorithm
GPU programming
Classification rules},
}
%0 Journal Article
%T A novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs
%A Beheshti Roui, Mohammad
%A Zomorodi-Moghadam, Mariam
%A Sarvelayati, Masoomeh
%A Moloud Abdar
%A Noori, Hamid
%A Pawel Plawiak
%A Ryszard Tadeusiewicz
%A Xujuan Zhou
%A Abbas Khosravi
%A Saeid Nahavandi
%A U. Rajendra Acharya
%J Knowledge-Based Systems
%@ 0950-7051
%D 2021
