• 논리연구
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• 제17권2호
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• pp.349-358
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• 2014

불완전한 지식의 문제는 오랫동안 인간이 해결하려는 것이었다. 인공지능에서 불완전한 지식의 문제를 다루기 위해 파블락은 러프집합론을 1982년에 제안하였다. 러프집합론은 다음과 같은 두 가지 흥미있는 성질을 가지고 있다. 먼저 하나의 러프집합은 지식기반에 따라 같은 집합이 아닌 다른 집합으로 간주된다는 것이다. 그리고 서로 다른 러프집합도 어떤 지식 기반에서 보면 서로 같은 집합으로 여겨진다는 것이다. 이러한 성질은 의미있는 철학적 해석을 낳는다. 즉 하나의 개념이나 사건은 다른 철학적 관점에서 다른 것으로 이해되기도 하고, 서로 다른 개념이나 사건도 어떤 관점에 따라서는 같은 것으로 간주될 수 있다는 것이다. 본고에서는 이러한 러프집합의 성질은 비판적 실재론이나 과학철학에서 관찰의 이론적재성을 지지하는 수학적 모델로 취급될 수 있다고 주장한다.

• 한국경영과학회지
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• 제25권3호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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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.

• 한국정보시스템학회지:정보시스템연구
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• 제14권1호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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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.

• 대한전기학회논문지:전력기술부문A
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• 제48권2호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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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.

• 산업경영시스템학회지
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• 제28권1호
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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.