• Title, Summary, Keyword: Information Granules

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Associations Among Information Granules and Their Optimization in Granulation-Degranulation Mechanism of Granular Computing

  • Pedrycz, Witold
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.13 no.4
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    • pp.245-253
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    • 2013
  • Knowledge representation realized by information granules is one of the essential facets of granular computing and an area of intensive research. Fuzzy clustering and clustering are general vehicles to realize formation of information granules. Granulation - degranulation paradigm is one of the schemes determining and quantifying functionality and knowledge representation capabilities of information granules. In this study, we augment this paradigm by forming and optimizing a collection of associations among original and transformed information granules. We discuss several transformation schemes and analyze their properties. A series of numeric experiments is provided using which we quantify the improvement of the degranulation mechanisms offered by the optimized transformation of information granules.

GA-Based Construction of Fuzzy Classifiers Using Information Granules

  • Kim Do-Wan;Lee Ho-Jae;Park Jin-Bae;Joo Young-Hoon
    • International Journal of Control, Automation, and Systems
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    • v.4 no.2
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    • pp.187-196
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    • 2006
  • A new GA-based methodology using information granules is suggested for the construction of fuzzy classifiers. The proposed scheme consists of three steps: selection of information granules, construction of the associated fuzzy sets, and tuning of the fuzzy rules. First, the genetic algorithm (GA) is applied to the development of the adequate information granules. The fuzzy sets are then constructed from the analysis of the developed information granules. An interpretable fuzzy classifier is designed by using the constructed fuzzy sets. Finally, the GA is utilized for tuning of the fuzzy rules, which can enhance the classification performance on the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes). To show the effectiveness of the proposed method, an example, the classification of the Iris data, is provided.

DCClass: a Tool to Extract Human Understandable Fuzzy Information Granules for Classification

  • Castellano, Giovanna;Fanelli, Anna M.;Mencar, Corrado
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.376-379
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    • 2003
  • In this paper we describe DCClass, a tool for fuzzy information granulation with transparency constraints. The tool is particularly suited to solve fuzzy classification problems, since it is able to automatically extract information granules with class labels. For transparency pursuits, the resulting information granules are represented in form of fuzzy Cartesian product of one-dimensional fuzzy sets. As a key feature, the proposed tool is capable to self-determining the optimal granularity level of each one-dimensional fuzzy set by exploiting class information. The resulting fun information granules can be directly translated in human-comprehensible fuzzy rules to be used for class inference. The paper reports preliminary experimental results on a medical diagnosis problem that shows the utility of the proposed tool.

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Design of Fuzzy Model for Data Mining

  • Kim, Do-Wan;Joo, Young-Hoon;Park, Jin-Bae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.1
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    • pp.107-113
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    • 2003
  • A new GA-based methodology using information granules is suggested for the construction of fuzzy classifiers. The proposed scheme consists of three steps: selection of information granules, construction of the associated fuzzy sets, and tuning of the fuzzy rules. First, the genetic algorithm (GA) is applied to the development of the adequate information granules. The fuzzy sets are then constructed from the analysis of the developed information granules. An interpretable fuzzy classifier is designed by using the constructed fuzzy sets. Finally, the GA are utilized for tuning of the fuzzy rules, which can enhance the classification performance on the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes). To show the effectiveness of the proposed method, an example, the classification of the Iris data, is provided.

A Construction of Fuzzy Model for Data Mining

  • Kim, Do-Wan;Joo, Young-Hoon;Park, Jin-Bae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.2
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    • pp.209-215
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    • 2003
  • A new GA-based methodology using information granules is suggested for the construction of fuzzy classifiers. The proposed scheme consists of three steps: selection of information granules, construction of the associated fuzzy sets, and tuning of the fuzzy rules. First, the genetic algorithm (GA) is applied to the development of the adequate information granules. The fuzzy sets are then constructed from the analysis of the developed information granules. An interpretable fuzzy classifier is designed by using the constructed fuzzy sets. Finally, the GA are utilized for tuning of the fuzzy rules, which can enhance the classification performance on the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes). To show the effectiveness of the proposed method, an example, the classification of the Iris data, is provided.

Fuzzy Modeling for Data Mining Using Information Granules

  • Kim, Do-Wan;Kim, Moon-Hwan;Joo, Young-Hoon;Park, Jin-Bae
    • 제어로봇시스템학회:학술대회논문집
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    • pp.111.4-111
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    • 2002
  • 1. Introduction 2. Information Granules 3. The proposed fuzzy modeling scheme 4. Simulation: Iris data 5. Conclusions

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

  • Pedrycz, Witold
    • Journal of Information Processing Systems
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    • v.7 no.3
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    • pp.397-412
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    • 2011
  • Granular Computing has emerged as a unified and coherent framework of designing, processing, and interpretation of information granules. Information granules are formalized within various frameworks such as sets (interval mathematics), fuzzy sets, rough sets, shadowed sets, probabilities (probability density functions), to name several the most visible approaches. In spite of the apparent diversity of the existing formalisms, there are some underlying commonalities articulated in terms of the fundamentals, algorithmic developments and ensuing application domains. In this study, we introduce two pivotal concepts: a principle of justifiable granularity and a method of an optimal information allocation where information granularity is regarded as an important design asset. We show that these two concepts are relevant to various formal setups of information granularity and offer constructs supporting the design of information granules and their processing. A suite of applied studies is focused on knowledge management in which case we identify several key categories of schemes present there.

Concepts and Design Aspects of Granular Models of Type-1 and Type-2

  • Pedrycz, Witold
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.15 no.2
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    • pp.87-95
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    • 2015
  • In this study, we pursue a new direction for system modeling by introducing the concept of granular models, which produce results in the form of information granules (such as intervals, fuzzy sets, and rough sets). We present a rationale and several key motivating arguments behind the use of granular models and discuss their underlying design processes. The development of the granular model includes optimal allocation of information granularity through optimizing the criteria of coverage and specificity. The emergence and construction of granular models of type-2 and type-n (in general) is discussed. It is shown that achieving a suitable coverage-specificity tradeoff (compromise) is essential for developing granular models.

Optimization of fuzzy systems based on information granules (정보 Granules 기반 퍼지 시스템의 최적화)

  • Park, Keon-Jun;Lee, Dong-Yoon;Oh, Sung-Kwun
    • Proceedings of the KIEE Conference
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    • pp.2567-2569
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    • 2003
  • 본 논문은 비선형 시스템의 퍼지모델을 위해 정보 Granules 기반 퍼지추론 시스템 모델의 최적화를 제시한다. 퍼지모델은 주로 경험적 방법에 의해 추출되기 때문에 보다 구체적이고 체계적인 방법에 의한 동정 및 최적화 될 필요성이 요구된다. 제안된 규칙베이스 퍼지모델은 HCM 클러스터링 방법, 컴플렉스 알고리즘 및 퍼지추론 방법을 이용하여 시스템 구조와 파라미터 동정을 수행한다. 두 가지 형태의 퍼지모델 추론 방법은 간략추론, 선형추론에 의해 시행된다. 본 논문에서는 퍼지모델의 입력변수와 퍼지 입력 공간 분할 및 입출력 데이타의 중심값을 구해서 후반부 다항식함수에 의한 정보 Granules 기반 구조 동정과 파라미터 동정을 통해 비선형 시스템을 표현한다. 전반부 파라미터의 동정에는 HCM 클러스터링 방법과 컴플렉스 알고리즘을 사용하고, 후반부는 표준 HCM 클러스터링과 표준 최소자승법을 사용하여 동정한다. 그리고 학습 및 테스트 데이타의 성능견과의 상호균형을 얻기 위한 하중값을 가진 성능지수를 제시함으로써 근사화와 예측성능의 향상을 꾀한다. 제안된 비선형 모델의 성능평가를 통해 그 우수성을 보인다.

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Scanning Electron Microscopic Study on the Tissue Mast Cells of Mammals (포유류 조직비만세포의 주사전자현미경적 연구)

  • Kang, H.S.;Yoo, K.S.
    • Applied Microscopy
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    • v.13 no.1
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    • pp.41-47
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    • 1983
  • A Scanning electron microscope which can obtain additional information not readily available with either the light or transmission electron microscope was used to study the mast cell shape and its granules in normal mammal tissue(rat mesentery, stomach and mouse stomach) by fretting cut using liquid nitrogen. The results showed that rat mesentery and mouse stomach mast cell surfaces had no ridges and microvilli, but revealed several microvilli projecting into the surrounding connective tissue in the rat stomach mast cell. The shape of the mast cell varied from discoid(in the rat mesenteric mast cell) to ellipsoid (rat and mouse stomach), ranging from 7.5 to $10{\mu}m$ in diameter. The shape of the nucleus was ellipsoid and nucleic membrane was adherent to the outer surface of the granules. The granules, approximately 0.2 to $0.9{\mu}m$ in diameter, were various shapes. Frequently, rounded protrusions of cytoplasmic granules could be discerned under the cell membrane. Many small granules were seen in the cytoplasm.

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