FR–KDE: A Hybrid Fuzzy Rule-Based Information Fusion Method with its Application in Biomedical Classification

Song, Xingjian; Qin, Bowen; Xiao, Fuyuan

doi:10.1007/s40815-020-00957-z

Cited by 14 publications

(4 citation statements)

References 77 publications

(80 reference statements)

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“…This approach is used to exploit the complete potential of SVM to diagnose the disease. In [15], a novel technique named FR-KDE was proposed. This technique is a combination of FR and Kernel Density Estimation (KDE) from Dempster-Shafer theory.…”

Section: Related Workmentioning

confidence: 99%

Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

Kalpana¹,

Dhanasekaran²,

Abirami³

et al. 2023

Computer Systems Science and Engineering

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Biomedical data classification has become a hot research topic in recent years, thanks to the latest technological advancements made in healthcare. Biomedical data is usually examined by physicians for decision making process in patient treatment. Since manual diagnosis is a tedious and time consuming task, numerous automated models, using Artificial Intelligence (AI) techniques, have been presented so far. With this motivation, the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI (BDC-CMBOAI) technique. The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data. Besides, the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection (CMBO-FS) technique to derive a useful subset of features. In addition, Ridge Regression (RR) model is also utilized as a classifier to identify the existence of disease. The novelty of the current work is its designing of CMBO-FS model for data classification. Moreover, CMBO-FS technique is used to get rid of unwanted features and boosts the classification accuracy. The results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.

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Section: Related Workmentioning

confidence: 99%

Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

Kalpana¹,

Dhanasekaran²,

Abirami³

et al. 2023

Computer Systems Science and Engineering

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“…These systems should be directly interpretable, i.e., they should explain their behavior in a way that they can be understood [14,15]. Clear examples can be found in problems in healthcare [16] and in particular in bio-medicine [17], banking advice, insurance [18], legal decision-making, robotics, planning, and many others. Therefore, there is a need to continue investigating the improvement of machine learning techniques with an inherent high explanatory power, as the most appropriate alternative ''for high-stakes prediction applications that deeply impact human lives'' [14].…”

Section: Introductionmentioning

confidence: 99%

Transparent but Accurate Evolutionary Regression Combining New Linguistic Fuzzy Grammar and a Novel Interpretable Linear Extension

Biedma-Rdguez

Gacto

Anguita‐Ruiz

et al. 2022

Int. J. Fuzzy Syst.

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Scientists must understand what machines do (systems should not behave like a black box), because in many cases how they predict is more important than what they predict. In this work, we propose a new extension of the fuzzy linguistic grammar and a mainly novel interpretable linear extension for regression problems, together with an enhanced new linguistic tree-based evolutionary multiobjective learning approach. This allows the general behavior of the data covered, as well as their specific variability, to be expressed as a single rule. In order to ensure the highest transparency and accuracy values, this learning process maximizes two widely accepted semantic metrics and also minimizes both the number of rules and the model mean squared error. The results obtained in 23 regression datasets show the effectiveness of the proposed method by applying statistical tests to the said metrics, which cover the different aspects of the interpretability of linguistic fuzzy models. This learning process has obtained the preservation of high-level semantics and less than 5 rules on average, while it still clearly outperforms some of the previous state-of-the-art linguistic fuzzy regression methods for learning interpretable regression linguistic fuzzy systems, and even to a competitive, pure accuracy-oriented linguistic learning approach. Finally, we analyze a case study in a real problem related to childhood obesity, and a real expert carries out the analysis shown.

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“…The Dempster-Shafer evidence theory was first proposed by Dempster [ 13 ] in 1967, and then further developed by his student Shafer [ 14 ] in 1976. This theory is a reasoning theory that can effectively deal with uncertain information [ 20 , 21 ], and is widely used in many fields, such as fault diagnosis [ 22 , 23 ], decision making [ 24 , 25 , 26 ], risk assessment [ 27 , 28 ], classification [ 29 , 30 , 31 ], and so on [ 32 , 33 ], which solves many problems caused by uncertain information. However, in application, the classic combination rule of DS theory has been found to have some problems.…”

Section: Introductionmentioning

confidence: 99%

Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion

Chen

Tang

2021

Entropy

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Dempster-Shafer (DS) evidence theory is widely used in various fields of uncertain information processing, but it may produce counterintuitive results when dealing with conflicting data. Therefore, this paper proposes a new data fusion method which combines the Deng entropy and the negation of basic probability assignment (BPA). In this method, the uncertain degree in the original BPA and the negation of BPA are considered simultaneously. The degree of uncertainty of BPA and negation of BPA is measured by the Deng entropy, and the two uncertain measurement results are integrated as the final uncertainty degree of the evidence. This new method can not only deal with the data fusion of conflicting evidence, but it can also obtain more uncertain information through the negation of BPA, which is of great help to improve the accuracy of information processing and to reduce the loss of information. We apply it to numerical examples and fault diagnosis experiments to verify the effectiveness and superiority of the method. In addition, some open issues existing in current work, such as the limitations of the Dempster-Shafer theory (DST) under the open world assumption and the necessary properties of uncertainty measurement methods, are also discussed in this paper.

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FR–KDE: A Hybrid Fuzzy Rule-Based Information Fusion Method with its Application in Biomedical Classification

Cited by 14 publications

References 77 publications

Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

Transparent but Accurate Evolutionary Regression Combining New Linguistic Fuzzy Grammar and a Novel Interpretable Linear Extension

Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion

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