Paper Info
Title
A novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs
Abstract
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.
Year
DOI
Venue
2021
10.1016/j.knosys.2021.107419
Knowledge-Based Systems
Keywords
DocType
Volume
Data mining,Machine learning,Rule discovery,Genetic algorithm,GPU programming,Classification rules
Journal
231
ISSN
Citations 
PageRank 
0950-7051
0
0.34
References 
Authors
21
11
Name
Order
Citations
PageRank
Mohamad Beheshti Roui100.34
Mariam Zomorodi Moghadam2254.65
Masoomeh Sarvelayati300.34
Moloud Abdar412314.65
Hamid Noori59717.32
Pawel Plawiak6998.84
Ryszard Tadeusiewicz7956141.52
Xujuan Zhou816519.22
Abbas Khosravi97313.23
Saeid Nahavandi101545219.71
Rajendra Acharya U114666296.34