The design of fuzzy information granules: Tradeoffs between specificity and experimental evidence

Yu, Feihong; Pedrycz, Witold

doi:10.1016/j.asoc.2007.10.026

Cited by 55 publications

(23 citation statements)

References 8 publications

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“…In the first phase we divide the original data into segments that retain the experimental nature of the data. In the second phase we create a granular representation of each segment [6]. These two phases has competing goals, since we are trying to accommodate more information for experimental relevance and at the same time we demand to be more specific in each information granule.…”

Section: Information Granulationmentioning

confidence: 99%

“…These two phases has competing goals, since we are trying to accommodate more information for experimental relevance and at the same time we demand to be more specific in each information granule. Algorithmic optimization [6] approach aims to compromise these two conflicting goals.…”

Section: Information Granulationmentioning

confidence: 99%

“…The goal of information granulation is to better understand the problem and transform it into more tractable smaller parts, so that we have smaller sub problems with smaller computational complexity [6]. Information granules are established using set theory, rough sets, fuzzy sets, and shadow sets, etc., [6]. There are essential two steps in the granulation process, namely, segmentation and granular representation [19].…”

Section: Information Granulationmentioning

confidence: 99%

“…Window sizes (w = 2, 4, 5) were used so that the original time-series is divided into segments (granule) of equal data points. Finally, a fuzzy triangular membership function was designed based on the methods outlined [6,7] …”

Section: Fuzzy-granulation Applied To Temporal Gait Parametersmentioning

confidence: 99%

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Case Study on Assessment of Mild Traumatic Brain Injury Using Granular Computing

Bogale¹,

Yu²,

Sarkodie-Gyan³

et al. 2012

ENG

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Patients with mild traumatic brain injury complain about having balance and stability problems despite normal clinical examination. The objective of this study is to investigate the stride-to-stride gait variability of mTBI subjects while walking on treadmill under dual-task gait protocols. Fuzzy-granular computing algorithm is used to objectively quantify the stride-to-stride variability of temporal gait parameters. The degrees of similarity (DS) of temporal gait parameters in the dual tasks were determined from the corresponding granulated time-series. The mTBI group showed relatively smaller degree of similarity for all window sizes under the cognitive (dual) task walking, showing pronounced stride-to-stride variability. Different levels of DS among the mTBI subjects were observed. Individually, both healthy and mTBI group showed different DS under the two dual-tasks, reflecting the challenging level of the cognitive tasks while walking. The mean values of the temporal parameters for the mTBI group were different from the averaged normal reference. On the other hand, the individual variance analysis shows no significant differences between the normal and dual task values for some mTBI subjects. The granular approach however is able to reveal very fine differences and exhibited similar trends for all mTBI subjects. Different DS values among mTBI group could be indicative for the different severity level or the undergone rehabilitation process.

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Section: Information Granulationmentioning

confidence: 99%

Section: Information Granulationmentioning

confidence: 99%

Section: Information Granulationmentioning

confidence: 99%

Section: Fuzzy-granulation Applied To Temporal Gait Parametersmentioning

confidence: 99%

See 2 more Smart Citations

Case Study on Assessment of Mild Traumatic Brain Injury Using Granular Computing

Bogale¹,

Yu²,

Sarkodie-Gyan³

et al. 2012

ENG

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“…In designing these functions, we used an approach described in [4], [6], [7]. In this approach, the goal is to satisfy two objectives:…”

Section: Fuzzy Approachmentioning

confidence: 99%

Assessment of functional impairment in human locomotion: A fuzzy-motivated approach

Alaqtash

Sarkodie-Gyan

Kreinovich

2012

2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)

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Abstract-Many neurological disorders result in disordered motion. The effects of a disorder can be decrease by an appropriate rehabilitation. To make rehabilitation efficient, we need to monitor the patient and check how well he or she improves. In our previous papers, we proposed a fuzzy-based semi-heuristic method of gauging how well a patient improved. Surprisingly, this semi-heuristic method turned out to be more efficient that we expected. In this paper, we provide a justification for this efficiency.

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Large-Scale Time Series Clustering Based on Fuzzy Granulation and Collaboration

Wang

Zhang

et al. 2015

Int. J. Intell. Syst.

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Clustering a group of large-scale time series with the same length is a frequently met problem in real world. However, the existing clustering methods often show high computational cost and low efficiency when dealing with this problem. In this paper, we propose a granulation-based horizontal collaborative fuzzy clustering method for this problem. In this method, some new subgroups are built from the given large-scale time series group by segmenting all the time series with time alignment. Thus, all the subsequences in each subgroup have the same length, which are much smaller than the length of the original time series. Just because of the smaller length, the clustering of all subsequences in each subgroup can be easily carried out with lower computation cost and higher efficiency. How to aggregate the clustering results of all subgroups to obtain the clustering result of the given group of large-scale time series becomes the main task of this paper. To solve this problem, we carry out the horizontal collaborative fuzzy clustering on the last subgroup by collaborating the clustering information of the previous subgroups. To obtain much better performance, we first perform fuzzy information granulation on the original group of large-scale time series. After that, the original group of time series is transformed into a group of granular time series. Therefore, the collaborative clustering is carried out on the corresponding granular subgroups. Simulation experiments presented here illustrate the good performance of the algorithm of our method. C 2015 Wiley Periodicals, Inc.

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The design of fuzzy information granules: Tradeoffs between specificity and experimental evidence

Cited by 55 publications

References 8 publications

Case Study on Assessment of Mild Traumatic Brain Injury Using Granular Computing

Case Study on Assessment of Mild Traumatic Brain Injury Using Granular Computing

Assessment of functional impairment in human locomotion: A fuzzy-motivated approach

Large-Scale Time Series Clustering Based on Fuzzy Granulation and Collaboration

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