An intuitionistic fuzzy bireduct model and its application to cancer treatment

Jain, Pankhuri; Tiwari, Abhishek; Som, Tanmoy

doi:10.1016/j.cie.2022.108124

Cited by 11 publications

(4 citation statements)

References 53 publications

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“…Rough set-based approaches have been utilized in various applications like bankruptcy prediction 42 , attribute/feature subset selection 43 , 44 , cancer prediction 45 , 46 , etc. In addition, recently, several innovative hybrid models have emerged, blending the realms of fuzzy logic and non-randomized systems (NRSs).…”

Section: Related Workmentioning

confidence: 99%

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis

Raza,

Jamal,

Qureshi

et al. 2024

Sci Rep

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Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson’s patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson’s dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson’s disease analysis.

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Section: Related Workmentioning

confidence: 99%

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis

Raza,

Jamal,

Qureshi

et al. 2024

Sci Rep

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“…In recent years, numerous MADM approaches proposed in various environment, such as intuitionistic fuzzy environment [ 27 – 30 ]. In Pythagorean fuzzy environment [ 31 , 32 ], Fermatean fuzzy environment [ 33 – 35 ], interval-valued intuitionistic fuzzy environment [ 36 , 37 ], interval-valued Fermatean fuzzy environment [ 38 ] and other fuzzy environments [ 39 – 43 ].…”

Section: Introductionmentioning

confidence: 99%

A new multi-attribute decision making approach based on new score function and hybrid weighted score measure in interval-valued Fermatean fuzzy environment

Qin

Peng

et al. 2023

Complex Intell. Syst.

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Interval-valued Fermatean fuzzy sets (IVFFSs) were introduced as a more effective mathematical tool for handling uncertain information in 2021. In this paper, firstly, a novel score function (SCF) is proposed based on IVFFNs that can distinguish between any two IVFFNs. And then, the novel SCF and hybrid weighted score measure were used to construct a new multi-attribute decision-making (MADM) method. Besides, three cases are used to demonstrate that our proposed method can overcome the disadvantages that the existing approaches cannot obtain the preference orderings of alternatives in some circumstances and involves the existence of division by zero error in the decision procedure. Compared with the two existing MADM methods, our proposed approach has the highest recognition index and the lowest error rate of division by zero. Our proposed method provides a better approach to dealing with the MADM problem in the interval-valued Fermatean fuzzy environment.

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“…All the above mentioned articles presented FS techniques in IF framework. Further, Jain et al [24] combined IF set with rough set and established a bireduct model. This model was implemented to produce reduced datasets by simultaneously reducing instances and features.…”

Section: Introductionmentioning

confidence: 99%

Combining SMOTE and Intuitionistic Fuzzy rough assisted model for data reduction to handle imbalanced datasets

Tiwari

Tripathi

Pandey

et al. 2023

Preprint

Self Cite

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Due to continuous bombardment from different sources such as social media like Twitter, Facebook, Instagram etc. and day by day activities, the size of datasets is gradually increasing. Data is enlarging both in number of instances and features. Processing these datasets using traditional algorithms and techniques require more resources and exponential time. Moreover, these datasets usually consisted of various issues such as class imbalance, availability of irrelevant and/or redundant features, noise, problematic instances and different types of uncertainties that may lead to degrade the overall performances of the learning algorithms. Our aim is to keep the maximum number of informative features and instances that could help for distinguishing the different categories without conflict. This paper presents a novel approach by combining synthetic minority oversampling technique (SMOTE), Intuitionistic Fuzzy (IF) set theory, rough set theory (RST), different class ratio, and sample pair selection concepts to handle all the above mentioned issues. Firstly, we incorporate SMOTE with a new IF rough instance selection (IS) for the cleaning of problematic minority and majority class instances. Secondly, a novel IF rough feature selection (FS) by using different class ratio based on the concept of sample pair selection is presented to eradicate irrelevant and redundant features along with noise. Thirdly, a comprehensive experimental study is given to justify the effectiveness of the proposed techniques. Finally, we present a framework to enhance the prediction of blood–brain barrier penetrating peptides (bbbpp) to expedite drug delivery into the brain.

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An intuitionistic fuzzy bireduct model and its application to cancer treatment

Cited by 11 publications

References 53 publications

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis

A new multi-attribute decision making approach based on new score function and hybrid weighted score measure in interval-valued Fermatean fuzzy environment

Combining SMOTE and Intuitionistic Fuzzy rough assisted model for data reduction to handle imbalanced datasets

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