A Master Slave Parallel Genetic Algorithm for Feature Selection in High Dimensional Datasets
Shaveta Tatwani1, Ela Kumar2
1Shaveta Tatwani, Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi-110006, India.
2Ela Kumar, Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi-110006, India.
Manuscript received on 11 August 2019. | Revised Manuscript received on 20 August 2019. | Manuscript published on 30 September 2019. | PP: 378-384 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4184098319/19©BEIESP | DOI: 10.35940/ijrte.C4184.098319
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Abstract: Feature Selection in High Dimensional Datasets is a combinatorial problem as it selects the optimal subsets from N dimensional data having 2N possible subsets. Genetic Algorithms are generally a good choice for feature selection in large datasets, though for some high dimensional problems it may take varied amount of time – few seconds, few hours or even few days. Therefore, it is important to use Genetic Algorithms that can give quality results in reasonably acceptable time limit. For this purpose, it is becoming necessary to implement Genetic Algorithms in an efficient manner. In this paper, a Master Slave Parallel Genetic Algorithm is implemented as a Feature Selection procedure to diminish the time intricacies of sequential genetic algorithm. This paper describes the speed gains in parallel Master-Slave Genetic Algorithm and also discusses the theoretical analysis of optimal number of slaves required for an efficient master slave implementation. The experiments are performed on three high-dimensional gene expression data. As Genetic Algorithm is a wrapper technique and takes more time to find the importance of any feature, Information Gain technique is used first as pre-processing task to remove the irrelevant features.
Keywords: Feature Selection, Genetic Algorithm, High Dimensional Data, Machine Learning, Master-Slave.
Scope of the Article: Machine Learning