• International Journal of Control, Automation, and Systems
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• 제4권2호
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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.

• 한국지능시스템학회논문지
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• 제13권2호
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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.

• 한국지능시스템학회논문지
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• 제13권1호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권4호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.191-194
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• 2002

In this paper, a new GA-based methodology with information granules is suggested for construction of the fuzzy classifier. We deal with the selection of the fuzzy region as well as two major classification problems-the feature selection and the pattern classification. The proposed method consists of three steps: the selection of the fuzzy region, the construction of the fuzzy sets, and the tuning of the fuzzy rules. The genetic algorithms (GAs) are applied to the development of the information granules so as to decide the satisfactory fuzzy regions. Finally, the GAs are also applied to the tuning procedure of the fuzzy rules in terms of the management of 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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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

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1510-1515
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• 2005

This paper is concerned with a Logic-based Fuzzy Neural Networks (LFNN) with the aid of fuzzy granulation. As the underlying design tool guiding the development of the proposed LFNN, we concentrate on the context-based fuzzy clustering which builds information granules in the form of linguistic contexts as well as OR fuzzy neuron which is logic-driven processing unit realizing the composition operations of T-norm and S-norm. The design process comprises several main phases such as (a) defining context fuzzy sets in the output space, (b) completing context-based fuzzy clustering in each context, (c) aggregating OR fuzzy neuron into linguistic models, and (c) optimizing connections linking information granules and fuzzy neurons in the input and output spaces. The experimental examples are tested through two-dimensional nonlinear function. The obtained results reveal that the proposed model yields better performance in comparison with conventional linguistic model and other approaches.

• 산업경영시스템학회지
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• 제31권4호
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• pp.140-145
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• 2008

Fuzzy partition is a conceptual vehicle that encapsulates data into information granules. Fuzzy equalization concerns a process of building information granules that are semantically and experimentally meaningful. A few algorithms generating fuzzy partitions with fuzzy equalization have been suggested. Simulations and experiments have showed that fuzzy partition representing more characteristics of given input distribution usually produces meaningful results. In this paper, given two points and cardinality of fuzzy partition, we prove that it is not true that there always exists a fuzzy partition with fuzzy equalization in which two of points having peaks fall on the given two points. Then, we establish an algorithm that minimizes the maximum distance between given two points and adjacent points having peaks in the partition. A numerical example is presented to show the validity of the suggested algorithm.

• 전자공학회논문지CI
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• 제47권3호
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• pp.76-82
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• 2010

본 논문은 수치적인 입출력데이터로부터 언어적인 규칙을 생성시키기 위한 체계적인 접근방법으로써 정보입자(information granules)에 근거한 언어적인 모델(LM: Linguistic Model)을 발전시킨다. Pedrycz에 의해 소개된 언어적인 모델은 컨텍스트 기반 퍼지 클러스터링(CFC: Context-based Fuzzy Clustering)으로부터 얻어지는 퍼지 정보입자에 의해 수행되어지며, 이는 입력과 출력공간과 연관된 클러스터 된 데이터들의 동질성을 보존하도록 클러스터를 추정한다. 언어적인 모델의 효능성은 이전 연구에서 이미 증명되었음에도 불구하고 성능 측면에서 개선시킬 필요성이 있다. 따라서, 본 논문에서는 기존 언어적인 모델의 근사화와 일반화 성능을 모두 향상시키기 위해 언어적인 컨텍스트의 자동적인 생성, 바이어스항의 추가, 결론부 파라미터의 변형된 구조를 통해 이루어진다. 실험결과는 자동차 연료소비량 예측문제와 보스턴 housing 데이터를 통해 제안된 방법이 언어적인 모델뿐만 아니라 기존 방법들보다 우수함을 증명한다.