• Title/Summary/Keyword: Rough set theory

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A Philosophical Implication of Rough Set Theory (러프집합론의 철학적 함의)

  • Park, Chang Kyun
    • Korean Journal of Logic
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    • v.17 no.2
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    • pp.349-358
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    • 2014
  • Human being has attempted to solve the problem of imperfect knowledge for a long time. In 1982 Pawlak proposed the rough set theory to manipulate the problem in the area of artificial intelligence. The rough set theory has two interesting properties: one is that a rough set is considered as distinct sets according to distinct knowledge bases, and the other is that distinct rough sets are considered as one same set in a certain knowledge base. This leads to a significant philosophical interpretation: a concept (or an event) may be understood as different ones from different perspectives, while different concepts (or events) may be understood as a same one in a certain perspective. This paper claims that such properties of rough set theory produce a mathematical model to support critical realism and theory ladenness of observation in the philosophy of science.

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A Hybrid Credit Rating System using Rough Set Theory (러프집합을 이용한 통합형 채권등급 평가모형 구축에 관한 연구)

  • 박기남;이훈영;박상국
    • Journal of the Korean Operations Research and Management Science Society
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    • v.25 no.3
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    • pp.125-135
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    • 2000
  • Many different statistical and artificial intelligent techniques have been applied to improve the predictability of credit rating. Hybrid models and systems have also been developed by effectively combining different modeling processes or combining the outcomes of individual models. In this paper, we introduced the rough set theory and developed a hybrid credit rating system that combines individual outcomes in terms of rough set theory. An experiment was conducted to compare the prediction capability of the system with those of other methods. The proposed system based on rough set method outperformed the others.

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Inductive Learning Algorithm using Rough Set Theory (Rough Set 이론을 이용한 연역학습 알고리즘)

  • 방원철;변증남
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1997.10a
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    • pp.331-337
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    • 1997
  • In this paper we will discuss a type of inductive learning called learning from examples, whose task is to induce general descriptions of concepts from specific instances of these concepts. In many real life situations however new instances can be added to the set of instances. It is first proposed within the framework of rough set theory, for such cases, an algorithm to find minimal set of rules for decision tables without recalculation for overall set of instances. The method of learning presented here is based on a rough set concept proposed by Pawlak[2]. It is shown an algorithm to fund minimal set of rules using reduct change theorems giving criteria for minimum recalculation and an illustrative example.

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Determination of the Input/Output Relations and Rule Generation for Fuzzy Combustion Control System of Refuse Incinerator using Rough Set Theory (Rough Set 이론을 이용한 쓰레기 소각로의 퍼지제어 시스템을 위한 입출력 관계 설정 및 규칙 생성)

  • 방원철;변증남
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1997.11a
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    • pp.81-86
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    • 1997
  • It is proposed, for fuzzy combustion control system of refuse incinerator to find the relationship between inputs and outputs and to generate rules to control by using rough set theory. It is not easy to find out the corresponding inputs for each output and the control rules with incomplete or imprecise information consisting expert knowledge, process and manipulator values in the field, and operation manual for the given system. Most decision problems can be formulated employing decision table formalism. A decision table on fuzzy combustion control system for refuse incinerator is simplified and produces control(rules). The I/O realtions and the control rules found by rough set theory are compared with the previous result.

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Integration rough set theory and case-base reasoning for the corporate credit evaluation (러프집합이론과 사례기반추론을 결합한 기업신용평가 모형)

  • Roh, Tae-Hyup;Yoo Myung-Hwan;Han In-Goo
    • The Journal of Information Systems
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    • v.14 no.1
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    • pp.41-65
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    • 2005
  • The credit ration is a significant area of financial management which is of major interest to practitioners, financial and credit analysts. The components of credit rating are identified decision models are developed to assess credit rating an the corresponding creditworthiness of firms an accurately ad possble. Although many early studies demonstrate a priori which of these techniques will be most effective to solve a specific classification problem. Recently, a number of studies have demonstrate that a hybrid model integration artificial intelligence approaches with other feature selection algorthms can be alternative methodologies for business classification problems. In this article, we propose a hybrid approach using rough set theory as an alternative methodology to select appropriate attributes for case-based reasoning. This model uses rough specific interest lies in lthe stable combining of both rough set theory to extract knowledge that can guide dffective retrevals of useful cases. Our specific interest lies in the stable combining of both rough set theory and case-based reasoning in the problem of corporate credit rating. In addition, we summarize backgrounds of applying integrated model in the field of corporate credit rating with a brief description of various credit rating methodologies.

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Improvement of ID3 Using Rough Sets (라프셋 이론이 적용에 의한 ID3의 개선)

  • Chung, Hong;Kim, Du-Wan;Chung, Hwan-Mook
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1997.10a
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    • pp.170-174
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    • 1997
  • This paper studies a method for making more efficient classification rules in the ID3 using the rough set theory. Decision tree technique of the ID3 always uses all the attributes in a table of examples for making a new decision tree, but rough set technique can in advance eleminate dispensable attributes. And the former generates only one type of classification rules, but the latter generates all the possibles types of them. The rules generated by the rough set technique are the simplist from as proved by the rough set theory. Therefore, ID3, applying the rough set technique, can reduct the size of the table of examples, generate the simplist form of the classification rules, and also implement an effectie classification system.

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INCREMENTAL INDUCTIVE LEARNING ALGORITHM IN THE FRAMEWORK OF ROUGH SET THEORY AND ITS APPLICATION

  • Bang, Won-Chul;Bien, Zeung-Nam
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.06a
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    • pp.308-313
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    • 1998
  • In this paper we will discuss a type of inductive learning called learning from examples, whose task is to induce general description of concepts from specific instances of these concepts. In many real life situations, however, new instances can be added to the set of instances. It is first proposed within the framework of rough set theory, for such cases, an algorithm to find minimal set of rules for decision tables without recalculation for overcall set of instances. The method of learning presented here is base don a rough set concept proposed by Pawlak[2][11]. It is shown an algorithm to find minimal set of rules using reduct change theorems giving criteria for minimum recalculation with an illustrative example. Finally, the proposed learning algorithm is applied to fuzzy system to learn sampled I/O data.

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A Study on the Structure Optimization of Multilayer Neural Networks using Rough Set Theory (러프집합을 이용한 다층 신경망의 구조최적화에 관한 연구)

  • Chung, Young-June;Jun, Hyo-Byung;Sim, Kwee-Bo
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.48 no.2
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    • pp.82-88
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    • 1999
  • In this paper, we propose a new structure optimization method of multilayer neural networks which begin and carry out learning from a bigger network. This method redundant links and neurons according to the rough set theory. In order to find redundant links, we analyze the variations of all weights and output errors in every step of the learning process, and then make the decision table from their variation of weights and output errors. We can find the redundant links from the initial structure by analyzing the decision table using the rough set theory. This enables us to build a structure as compact as possible, and also enables mapping between input and output. We show the validity and effectiveness of the proposed algorithm by applying it to the XOR problem.

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3D Feature Detection using Rough Set Theory (러프 집합 이론을 이용한 3차원 물체 특징 추출)

  • Chung, Young-June;Jun, Hyo-Byung;Sim, Kwee-Bo
    • Proceedings of the KIEE Conference
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    • 1998.07g
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    • pp.2222-2224
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    • 1998
  • This paper presents a 3D feature extraction method using rough set theory. Using the stereo cameras, we obtain the raw images and then perform several processes including gradient computation and image matching process. Decision rule constructed via rough set theory determines whether a ceratin point in the image is 3D edge or not. We propose a method finding rules for 3D edge extraction using rough set.

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Discretization of Continuous Attributes based on Rough Set Theory and SOM (러브집합이론과 SOM을 이용한 연속형 속성의 이산화)

  • Seo Wan-Seok;Kim Jae-Yearn
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.28 no.1
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    • pp.1-7
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    • 2005
  • Data mining is widely used for turning huge amounts of data into useful information and knowledge in the information industry in recent years. When analyzing data set with continuous values in order to gain knowledge utilizing data mining, we often undergo a process called discretization, which divides the attribute's value into intervals. Such intervals from new values for the attribute allow to reduce the size of the data set. In addition, discretization based on rough set theory has the advantage of being easily applied. In this paper, we suggest a discretization algorithm based on Rough Set theory and SOM(Self-Organizing Map) as a means of extracting valuable information from large data set, which can be employed even in the case where there lacks of professional knowledge for the field.