Abstract
Feature selection is the tool required to study data with high dimensions in an easy way. It involves extracting attributes from a dataset having a large number of attributes in such a way so as the reduced attribute set can describe the dataset in a manner similar to that of the entire attribute set. Reducing the features of the data and selecting only the more relevant features reduce the computational and storage requirements which are needed to process the entire dataset. Rough set is the approach of approximating a conventional set. It is used in data mining for reduction of datasets and to find hidden pattern in datasets. This paper aims to devise an algorithm which performs feature selection on a given dataset using the concepts of rough set.
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Tibrewal, B., Chaudhury, G.S., Chakraborty, S., Kairi, A. (2019). Rough Set-Based Feature Subset Selection Technique Using Jaccard’s Similarity Index. In: Chakraborty, M., Chakrabarti, S., Balas, V., Mandal, J. (eds) Proceedings of International Ethical Hacking Conference 2018. Advances in Intelligent Systems and Computing, vol 811. Springer, Singapore. https://doi.org/10.1007/978-981-13-1544-2_39
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DOI: https://doi.org/10.1007/978-981-13-1544-2_39
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