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

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Shreevastava S.; SINGH S.; Tiwari A. K.; Som T.

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Journal Paper

Paper Information

Journal: Iranian Journal of Fuzzy Systems Year:2021 | Volume:18 | Issue:6 Page(s): 67-82

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Title

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

Author(s)

Shreevastava S. | SINGH S. | Tiwari A. K. | Som T. | Issue Writer Certificate

Keywords

Attribute selectionQ2

rough setQ2

intuitionistic fuzzy setQ2

dierent classes ratioQ2

Laplace summation operatorQ2

Abstract

In real-world data deluge, due to insigni cant 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 exible 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 Di erent Classes ratio and Laplace summation operator. Firstly, Di erent 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 classi cation accuracies.

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APA: Copy

Shreevastava, S., SINGH, S., Tiwari, A. K., & Som, T.. (2021). Di erent classes ratio and Laplace summation operator based intuitionistic fuzzy rough attribute selection. IRANIAN JOURNAL OF FUZZY SYSTEMS, 18(6 ), 67-82. SID. https://sid.ir/paper/413135/en

Vancouver: Copy

Shreevastava S., SINGH S., Tiwari A. K., Som T.. Di erent classes ratio and Laplace summation operator based intuitionistic fuzzy rough attribute selection. IRANIAN JOURNAL OF FUZZY SYSTEMS[Internet]. 2021;18(6 ):67-82. Available from: https://sid.ir/paper/413135/en

IEEE: Copy

S. Shreevastava, S. SINGH, A. K. Tiwari, and T. Som, “Di erent classes ratio and Laplace summation operator based intuitionistic fuzzy rough attribute selection,” IRANIAN JOURNAL OF FUZZY SYSTEMS, vol. 18, no. 6 , pp. 67–82, 2021, [Online]. Available: https://sid.ir/paper/413135/en

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