The Principle of Justifiable Granularity and an Optimization of Information Granularity Allocation as Fundamentals of Granular Computing

Pedrycz, Witold

doi:10.3745/jips.2011.7.3.397

Cited by 70 publications

(7 citation statements)

References 25 publications

(22 reference statements)

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“…The purpose of this principle [25] is to specify the optimal size of an information granule where sufficient coverage for experimental data exists while simultaneously limiting the coverage size in order to not overgeneralize. These differences are shown in Figure 4.…”

Section: Principle Of Justifiable Granularitymentioning

confidence: 99%

Hybrid Learning for General Type-2 TSK Fuzzy Logic Systems

et al. 2017

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This work is focused on creating fuzzy granular classification models based on general type-2 fuzzy logic systems when consequents are represented by interval type-2 TSK linear functions. Due to the complexity of general type-2 TSK fuzzy logic systems, a hybrid learning approach is proposed, where the principle of justifiable granularity is heuristically used to define an amount of uncertainty in the system, which in turn is used to define the parameters in the interval type-2 TSK linear functions via a dual LSE algorithm. Multiple classification benchmark datasets were tested in order to assess the quality of the formed granular models; its performance is also compared against other common classification algorithms. Shown results conclude that classification performance in general is better than results obtained by other techniques, and in general, all achieved results, when averaged, have a better performance rate than compared techniques, demonstrating the stability of the proposed hybrid learning technique.

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Section: Principle Of Justifiable Granularitymentioning

confidence: 99%

Hybrid Learning for General Type-2 TSK Fuzzy Logic Systems

et al. 2017

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“…An important consideration will be the incorpo-ration of joint spatial and temporal characteristics [29]. Another key area of research will be dealing with imprecise data acquired from disparate database repositories at varying levels of information granularity that may be tackled with the latest soft computing approaches in collaborative clustering and granular computing [52] [53].…”

Section: Prescriptionmentioning

confidence: 99%

On the Role of Soft Computing in Modelling the Spread of Infectious Diseases

Pizzi¹

2013

Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology

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Infectious disease modelling is an interdisciplinary activity that has become more prevalent over the last decade as a valuable and practical tool for public health experts. Soft computing, with its suite of techniques and strategies, which effectively handles imprecise, approximate, and vague scenarios, can play an important role in expanding the use of these models. We present a review of soft computing techniques that have been used in infectious disease modelling as well as a prescription of future challenges and issues this discipline must address to be fully accepted by the public health community.

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“…In order to construct a higher-level representation method of the time series model, a time series modeling method based on granular computing is further proposed by researchers ( Lu, Chen & Pedrycz, 2015 ; Froelich & Pedrycz, 2017 ). Granular computing ( Bargiela & Pedrycz, 2008 ) is a human-centered information processing framework platform, which uses “reasonable information granulation principle” ( Pedrycz, 2011 ; Pedrycz & Homenda, 2013 ) to divide complex and abstract information into simple and understandable information granules according to certain rules. This method does not excessively pursue the accurate value of the model, but mediates the “precision” and “interpretability” of the model so that the dynamic behavior of time series is easier to be understood.…”

Section: Introductionmentioning

confidence: 99%

Modeling time series by aggregating multiple fuzzy cognitive maps

Gan

Feng

2021

PeerJ Computer Science

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Background The real time series is affected by various combinations of influences, consequently, it has a variety of variation modality. It is hard to reflect the variation characteristic of the time series accurately when simulating time series only by a single model. Most of the existing methods focused on numerical prediction of time series. Also, the forecast uncertainty of time series is resolved by the interval prediction. However, few researches focus on making the model interpretable and easily comprehended by humans. Methods To overcome this limitation, a new prediction modelling methodology based on fuzzy cognitive maps is proposed. The bootstrap method is adopted to select multiple sub-sequences at first. As a result, the variation modality are contained in these sub-sequences. Then, the fuzzy cognitive maps are constructed in terms of these sub-sequences, respectively. Furthermore, these fuzzy cognitive maps models are merged by means of granular computing. The established model not only performs well in numerical and interval predictions but also has better interpretability. Results Experimental studies involving both synthetic and real-life datasets demonstrate the usefulness and satisfactory efficiency of the proposed approach.

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The Principle of Justifiable Granularity and an Optimization of Information Granularity Allocation as Fundamentals of Granular Computing

Cited by 70 publications

References 25 publications

Hybrid Learning for General Type-2 TSK Fuzzy Logic Systems

Hybrid Learning for General Type-2 TSK Fuzzy Logic Systems

On the Role of Soft Computing in Modelling the Spread of Infectious Diseases

Modeling time series by aggregating multiple fuzzy cognitive maps

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