IFC-BD: An Interpretable Fuzzy Classifier for Boosting Explainable Artificial Intelligence in Big Data

Aghaeipoor, Fatemeh; Javidi, Mohammad Masoud; Fernández, Alberto

doi:10.1109/tfuzz.2021.3049911

Cited by 22 publications

(9 citation statements)

References 32 publications

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“…Internal Operation Architecture under Big Data and Internet of Things Technology. Under the technology of big data and Internet of things, in the process of metabolic prediction of human function, first determine the active intrusion intention of complex network [18][19][20], establish the set function of intrusion intention and attack behavior, and estimate the diffusion equation of active intrusion metabolism of complex network on this basis. Based on the diffusion equation, a grey prediction model of active intrusion metabolism in complex networks is constructed [21,22].…”

Section: Realize the Metabolic Grey Prediction Modelmentioning

confidence: 99%

The Metabolism Grey Prediction Model Based on Big Data and Internet of Things Technology

Ren

Sun

Shan

et al. 2022

Wireless Communications and Mobile Computing

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In view of the uncertainty and diversity of the metabolic grey prediction model in the prediction process, resulting in poor prediction effect, a metabolic grey prediction model based on big data and Internet of things technology is constructed. Establish the aerobic metabolic process of human telecontrol and put forward the detection index of aerobic metabolic cycle function in human telecontrol; on this basis, use the metabolic grey prediction model analysis algorithm to determine the active intrusion intention of complex network, establish the intrusion intention attack behavior set function, establish the internal operation architecture under the technology of big data and Internet of things, and realize the construction of metabolic grey prediction model. The experimental results show that the constructed model can realize data prediction, with high confidence level and good effect.

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Section: Realize the Metabolic Grey Prediction Modelmentioning

confidence: 99%

The Metabolism Grey Prediction Model Based on Big Data and Internet of Things Technology

Ren

Sun

Shan

et al. 2022

Wireless Communications and Mobile Computing

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“…A key advantageous property of fuzzy systems is that the models are transparent, and more interpretable and understandable to humans. Therefore, recently a lot of work has been done to explain complex ML and DL models using fuzzy systems [16][17][18][19]. This strength of Fuzzy Modeling (FM) is also exploited in [20], and FM is used in conjunction with Granular Computing into the XAI models to enable them to be interpretable and explainable.…”

Section: Explainable Aimentioning

confidence: 99%

Towards a Framework for Interpretation of CNN Results with ANFIS

Ismail

Shang

Shen

2021

Advances in Intelligent Systems and Computing

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The performance of Deep Convolutional Neural Network (CNN) in the field of computer vision and image processing has shown tremendously amazing results. Nevertheless, the internal hidden and deep network architecture of CNN offers a Black Box computational model. Thus, it is infeasible to interpret and explain the computed results directly from such a model, whilst semantic interpretation and explanation are often required for applications such as fault monitoring in a physical plant and medical diagnosis in a human body. To address this challenging problem, various approaches have been proposed in the emerging area of eXplainable AI. Adaptive Network-based Fuzzy Inference System (ANFIS) provides a possible modelling technique that returns transparent and interpretable reasoning results, but it tends to work with data of a dimensionality considerably lower than that workable with CNN. Yet, an integration of these two different approaches may present a meaningful solution to the problem. This paper introduces such a novel framework which enables an interpretable explanation of the CNN outcomes with an ANFIS. This is facilitated by the use of Fuzzy Rough Feature Selection that converts high dimensional data into low dimensional data while minimising information loss. Initial experimental results illustrate the working of this framework.

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“…The recent rapid progress in machine learning has led to resurgence in interest in explainable artificial intelligence (XAI). Hagras [30] summarized three approaches to realize XAI: 1) deep explanation: the use of deep learning techniques to learn explainable features, like some successful achievements in automatic ship classification [40] and pattern recognition applications [4]; 2) interpretable models: techniques to learn more structured, interpretable, and causal models, e.g., rulebased modeling whose latest achievements include interpretable fuzzy classifier for big data [1] and deep convolutional fuzzy systems (DCFS) to stock index prediction [46]; and, 3) model induction: techniques to infer explainable model from any model as a black box, e.g. critical thinking about XAI for rule-based fuzzy systems [38].…”

Section: Introductionmentioning

confidence: 99%

Highly explainable cumulative belief rule-based system with effective rule-base modeling and inference scheme

Yang

et al. 2022

Knowledge-Based Systems

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IFC-BD: An Interpretable Fuzzy Classifier for Boosting Explainable Artificial Intelligence in Big Data

Cited by 22 publications

References 32 publications

The Metabolism Grey Prediction Model Based on Big Data and Internet of Things Technology

The Metabolism Grey Prediction Model Based on Big Data and Internet of Things Technology

Towards a Framework for Interpretation of CNN Results with ANFIS

Highly explainable cumulative belief rule-based system with effective rule-base modeling and inference scheme

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