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Intuitionistic Fuzzy Quantifier and Its Application in Feature Selection

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Abstract

Nowadays, databases expand rapidly due to electronically generated information from different fields like bioinformatics, census data, social media, business transactions, etc. Hence, feature selection/attribute reduction in databases is necessary in order to reduce time, cost, storage and noise for better accuracy. For this purpose, the rough set theory has been played a very significant role, but this theory is inefficient in case of real-valued data set due to information loss through discretization process. Hybridization of rough set with intuitionistic fuzzy set successfully dealt with this issue, but it may radically change the outcome of the approximations by adding or ignoring a single element. To handle this situation, we reconsider the hybridization process by introducing intuitionistic fuzzy quantifiers into the idea of upper and lower approximations. Supremacy of intuitionistic fuzzy quantifier over VPRS and VQRS is presented with the help of some examples. A novel process for feature selection is given by using the degree of dependency approach with intuitionistic fuzzy quantifier-based lower approximation. A greedy algorithm along with two supportive examples is presented in order to demonstrate the proposed approach. Finally, proposed algorithm is implemented on some benchmark datasets and classification accuracies for different classifiers are compared.

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Acknowledgements

First author would like to thank the Research Foundation-CSIR for funding her research.

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Authors and Affiliations

  1. DST-Centre for Interdisciplinary Mathematical Sciences, Institute of Science, BHU, Varanasi, 221005, India

    Shivani Singh

  2. Department of Mathematical Sciences, IIT (BHU), Varanasi, 221005, India

    Shivam Shreevastava, Tanmoy Som & Pankhuri Jain

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  1. Shivani Singh
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  2. Shivam Shreevastava
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Correspondence to Shivam Shreevastava.

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Singh, S., Shreevastava, S., Som, T. et al. Intuitionistic Fuzzy Quantifier and Its Application in Feature Selection. Int. J. Fuzzy Syst. 21, 441–453 (2019). https://doi.org/10.1007/s40815-018-00603-9

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  • DOI: https://doi.org/10.1007/s40815-018-00603-9

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