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Shreevastava, S., Singh, S., Tiwari, A. K., Som, T.. (1400). Different classes ratio and Laplace summation operator based intuitionistic fuzzy rough attribute selection. نشریه علمی دانشگاه سیستان و بلوچستان, 18(6), 67-82. doi: 10.22111/ijfs.2021.6334
S. Shreevastava; S. Singh; A. K. Tiwari; T. Som. "Different classes ratio and Laplace summation operator based intuitionistic fuzzy rough attribute selection". نشریه علمی دانشگاه سیستان و بلوچستان, 18, 6, 1400, 67-82. doi: 10.22111/ijfs.2021.6334
Shreevastava, S., Singh, S., Tiwari, A. K., Som, T.. (1400). 'Different classes ratio and Laplace summation operator based intuitionistic fuzzy rough attribute selection', نشریه علمی دانشگاه سیستان و بلوچستان, 18(6), pp. 67-82. doi: 10.22111/ijfs.2021.6334
Shreevastava, S., Singh, S., Tiwari, A. K., Som, T.. Different classes ratio and Laplace summation operator based intuitionistic fuzzy rough attribute selection. نشریه علمی دانشگاه سیستان و بلوچستان, 1400; 18(6): 67-82. doi: 10.22111/ijfs.2021.6334

Different classes ratio and Laplace summation operator based intuitionistic fuzzy rough attribute selection

Iranian Journal of Fuzzy Systems
مقاله 7، دوره 18، شماره 6، بهمن و اسفند 2021، صفحه 67-82  XML اصل مقاله (239.98 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22111/ijfs.2021.6334
نویسندگان
S. Shreevastava* 1؛ S. Singh2؛ A. K. Tiwari3؛ T. Som4
1Division of Mathematics, SBAS, Galgotias University, Greater Noida, UP- 201310
2DST-Centre for Interdisciplinary Mathematical Sciences, Institute of Science, BHU, Varanasi, UP-221005
3Department of CSE, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana -500075
4Department of Mathematical Sciences, IIT (BHU), Varanasi, UP -221005
چکیده
In real-world data deluge, due to insignificant information and high dimension, irrelevant and redundant attributes reduce the ability of experts both in predictive accuracy and speed, respectively. Attribute selection is the notion of selecting those attributes that are essential as well as enough to specify the target knowledge preferably. Fuzzy rough set-based approaches play a crucial role in selecting relevant and less redundant attributes from a high-dimensional dataset. Intuitionistic fuzzy set-based approaches can handle uncertainty as it gives an additional degree of freedom when compared to fuzzy approaches. So, it has a more flexible and practical ability to deal with vagueness and noise available in the information system. In this paper, we introduce two new robust approaches for attribute selection based on intuitionistic fuzzy rough set theory using the concepts of Different Classes ratio and Laplace Summation operator. Firstly, Different Classes ratio and Laplace Summation operator based lower and
upper approximations are established based on intuitionistic fuzzy rough set concept. Moreover, we present algorithms and illustrative examples for a better understanding of our approaches. Finally, experimental analysis is performed on some real-valued datasets for attribute selection and classification accuracies.
کلیدواژه‌ها
Attribute selection؛ rough set؛ intuitionistic fuzzy set؛ different classes ratio؛ Laplace summation operator
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