Particle Swarm Optimization based Feature Selection with Evolutionary Outlay-Aware Deep Belief Network Classifier (PSO-EOA-DBNC) for High Dimensional Datasets
DOI:
https://doi.org/10.26438/ijcse/v7i8.6169Keywords:
data mining, feature selection, particle swarm optimization, deep belief network, evolutionary algorithmAbstract
Data mining research extends its wings to several domains and classification is one of the thrust areas for researchers. The curse of dimensionality is reduced by many optimization techniques and machine learning algorithms. In this research work, a particle swarm optimization based feature selection method is employed to deal with the curse of dimensionality. The PSO algorithm makes use of the fitness function that is obtained from the evolutionary outlay aware deep belief network which conducts classification. 20 datasets are taken for evaluating the conductance of the PSO – EOA – DBNC in terms of classification accuracy and elapsed time. From the results it is significant to notice that PSO-EOA-DBNC out conducts than that of other classifiers.
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