Abstract
Due to the data explosion, Big Data is everywhere all around of us. The curse of dimensionality in Big Data has produced a great challenge for data classification problems. Feature selection is a crucial process to select the most important features to increase the classification accuracy and to reduce the time complexity. Traditional feature selection approaches suffer from various limitations, so Particle Swarm Optimization (PSO)-based feature selection approaches are proposed to overcome these limitations, but classical PSO shows premature convergence when the number of features increases or the datasets having more categories/classes. In this paper, topology-controlled Scale-Free Particle Swarm Optimization (SF-PSO) is proposed for feature selection in high-dimensional datasets. Multi-Class Support Vector Machine (MC-SVM) is used as a machine learning classifier and obtained results show the superiority of our proposed approach in big data classification.
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Gupta, S.L., Baghel, A.S., Iqbal, A. (2019). Big Data Classification Using Scale-Free Binary Particle Swarm Optimization. In: Yadav, N., Yadav, A., Bansal, J., Deep, K., Kim, J. (eds) Harmony Search and Nature Inspired Optimization Algorithms. Advances in Intelligent Systems and Computing, vol 741. Springer, Singapore. https://doi.org/10.1007/978-981-13-0761-4_109
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DOI: https://doi.org/10.1007/978-981-13-0761-4_109
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