A novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs
Article
Article Title | A novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs |
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ERA Journal ID | 18062 |
Article Category | Article |
Authors | Roui, Mohammad Beheshti (Author), Zomorodi, Mariam (Author), Sarvelayati, Masoomeh (Author), Abdar, Moloud (Author), Noori, Hamid (Author), Plawiak, Pawel (Author), Tadeusiewicz, Ryszard (Author), Zhou, Xujuan (Author), Khosravi, Abbas (Author), Nahavandi, Saeid (Author) and Acharya, U. Rajendra (Author) |
Journal Title | Knowledge-Based Systems |
Journal Citation | 231 |
Article Number | 107419 |
Number of Pages | 17 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0950-7051 |
1872-7409 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.knosys.2021.107419 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S095070512100681X |
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. |
Keywords | data mining; machine learning; rule discovery; genetic algorithm; GPU programming; classification rules |
ANZSRC Field of Research 2020 | 461199. Machine learning not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Institution of Origin | University of Southern Queensland |
Byline Affiliations | Ferdowsi University of Mashhad, Iran |
Deakin University | |
Cracow University of Technology, Poland | |
AGH University of Science and Technology, Poland | |
School of Business | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan | |
Singapore University of Social Sciences (SUSS), Singapore |
https://research.usq.edu.au/item/q6q0y/a-novel-approach-based-on-genetic-algorithm-to-speed-up-the-discovery-of-classification-rules-on-gpus
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