Abstract
Attribute selection considers more informative and important features and thereby the size of the dataset reduces. The reduction in dimensionality is achieved by removing inappropriate and unimportant features. Selecting features in supervised feature selection will help the search process to discover the prominent attributes for the classification of the given medical dataset. There are many evolutionary computation techniques and particle swarm optimization is one among them which always helps to identify the overall optimal solution in various applications. The present research work takes the advantages of particle swarm optimization and the entropy function. This paper proposes a Supervised PSO with an entropy function for feature selection which deals with a large medical dataset. The effectiveness of this proposed hybrid algorithm is evaluated and checked for error minimization and proved to be effective in the classification of medical domain.
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Rani, J.A.E., Kirubakaran, E., Juliet, S., Zoraida, B.S.E. (2022). Supervised Hybrid Particle Swarm Optimization with Entropy (PSO-ER) for Feature Selection in Health Care Domain. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1387. Springer, Singapore. https://doi.org/10.1007/978-981-16-2594-7_64
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DOI: https://doi.org/10.1007/978-981-16-2594-7_64
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