Abstract
The applications of feature selection increase rapidly due to its diverse areas. Classification is a part of feature selection, which is used in a large number of the dataset. Sometimes, due to a large number of features in high dimensional datasets, performance may decrease. So, in this chapter, an efficient feature selection technique is proposed by the fusion of central force optimization (CFO) and rough set. The CFO is used to optimize the several parameters of the proposed method, where the rough set is used to established relationship between noisy and imprecise information or the parameters. The combination of both helps to estimate uncertainties of the proposed problem. The proposed fusion method is an intelligent technique which is compared with the standard technique named as genetic algorithm (GA) based on some valid metrics. Finally, it shows that the proposed method outperforms the metrics in terms of accuracy and quality.
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Huda, R.K., Banka, H. (2021). Feature Selection Method Using CFO and Rough Sets for Medical Dataset. In: Das, S.K., Dao, TP., Perumal, T. (eds) Nature-Inspired Computing for Smart Application Design. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-33-6195-9_4
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DOI: https://doi.org/10.1007/978-981-33-6195-9_4
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