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Optimization of parallel SVM algorithm for big data.
- Source :
-
Journal of Computational Methods in Sciences & Engineering . 2024, Vol. 24 Issue 2, p1253-1266. 14p. - Publication Year :
- 2024
-
Abstract
- Parallel Support Vector Machine (SVM) based on big data has achieved some results in data mining, but due to the complexity of the data itself and a large amount of noisy data, its execution efficiency and classification accuracy in the big data environment are very low. In order to eliminate noise, a noise reduction method based on Noise Cleaning (NC) strategy was proposed, and redundant training samples in big data environments were deleted; Introduce an improved Artificial Fish Swarm Algorithm (IAFSA) to obtain the final Parallel SVM algorithm using mutual information and artificial fish swarm algorithm based on MapReduce (MIAFSA-PSVM) classification model. The results indicate that when compared to CMI-PSVM, the execution time of MIAFSA-PSVM algorithm is higher on the NDC dataset with the largest data size, The SVM parameter optimization algorithm based on MapReduce and cuckoo search (CSSVM-MR) and the particle swarm optimization based parallel support vector machine ensemble algorithm (PSO-PSVM) decreased by 40.1%, 79.3%, and 51.7%, respectively. This indicates that GIESVM-MR and MIAFSA-PSVM have strong adaptability to big data environments and high classification accuracy. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14727978
- Volume :
- 24
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Journal of Computational Methods in Sciences & Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 177228761
- Full Text :
- https://doi.org/10.3233/JCM-247335