• 대한의용생체공학회:의공학회지
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• 제31권1호
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• pp.40-49
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• 2010

A rule weight -based fuzzy classification model is proposed to analyze the patterns of admission-discharge of patients as a previous research for differential diagnosis of dyspnea. The proposed model is automatically generated from a labeled data set, supervised learning strategy, using three procedure methodology: i) select fuzzy partition regions from spatial distribution of data; ii) generate fuzzy membership functions from the selected partition regions; and iii) extract a set of candidate rules and resolve a conflict problem among the candidate rules. The effectiveness of the proposed fuzzy classification model was demonstrated by comparing the experimental results for the dyspnea patients' data set with 11 features selected from 55 features by clinicians with those obtained using the conventional classification methods, such as standard fuzzy classifier without rule weights, C4.5, QDA, kNN, and SVMs.

• Nonlinear Functional Analysis and Applications
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• 제26권5호
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• pp.935-947
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• 2021

An efficient adaptive scheme based on a triple mixed quadrature rule of precision nine for approximate evaluation of line integral of analytic functions has been constructed. At first, a mixed quadrature rule SM₁(f) has been formed using Gauss-Legendre three point transformed rule and five point Booles transformed rule. A suitable linear combination of the resulting rule and Clenshaw-Curtis seven point rule gives a new mixed quadrature rule SM₁₀(f). This mixed rule is termed as triple mixed quadrature rule. An adaptive quadrature scheme is designed. Some test integrals having analytic function integrands have been evaluated using the triple mixed rule and its constituent rules in non-adaptive mode. The same set of test integrals have been evaluated using those rules as base rules in the adaptive scheme. The triple mixed rule based adaptive scheme is found to be the most effective.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 추계학술대회-지능형 정보기술과 미래조직 Information Technology and Future Organization
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• pp.337-346
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• 1999

In this paper, we propose the rough set approach to extract trading rules able to discriminate between bullish and bearish markets in stock market. The rough set approach is very valuable to extract trading rules. First, it does not make any assumption about the distribution of the data. Second, it not only handles noise well, but also eliminates irrelevant factors. In addition, the rough set approach appropriate for detecting stock market timing because this approach does not generate the signal for trade when the pattern of market is uncertain. The experimental results are encouraging and prove the usefulness of the rough set approach for stock market analysis with respect to profitability.

• 대한산업공학회지
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• 제39권1호
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• pp.10-19
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• 2013

Dispatching rule plays an important role in a hybrid flow shop. Finding the appropriate dispatching rule becomes more challenging when there are multiple criteria, uncertain demands, and dynamic manufacturing environment. Using a single dispatching rule for the whole shop or a set of rules based on a single criterion is not sufficient. Therefore, a multi-criteria decision making technique using 'the order preference by similarity to ideal solution' (TOPSIS) and 'analytic hierarchy process' (AHP) is presented. The proposed technique is aimed to find the most suitable set of dispatching rules under different manufacturing scenarios. A simulation based case study on a PCB manufacturing process is presented to illustrate the procedure and effectiveness of the proposed methodology.

• Industrial Engineering and Management Systems
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• 제10권1호
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• pp.65-73
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• 2011

In rough set literatures, methods for inducing minimal rules from a given decision table have been proposed. When the decision attribute is ordinal, inducing rules about upward and downward unions of decision classes is advantageous in the simplicity of obtained rules. However, because of independent applications of the rule induction method, inclusion relations among upward/downward unions in conclusion parts are not inherited to the condition parts of obtained rules. This non-inheritance may debase the quality of obtained rules. To ensure that inclusion relations among conclusions are inherited to conditions, we propose two rule induction approaches. The performances of the proposed approaches considering the inclusion relations between conclusions are examined by numerical experiments.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.521-524
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• 2002

This paper deals with the rule generation from data for control system and data mining using rough set. If the cores and reducts are searched for without consideration of the frequency of data belonging to the same equivalent class, the unnecessary attributes may not be discarded, and the resultant rules don't represent well the characteristics of the data. To improve this, we handle the inconsistent data with a probability measure defined by support, As a result the effect of uncertainty in knowledge reduction can be reduced to some extent. Also we construct the rule base in a hierarchical structure by applying core as the classification criteria at each level. If more than one core exist, the coverage degree is used to select an appropriate one among then to increase the classification rate. The proposed method gives more proper and effective rule base in compatibility and size. For some data mining example the simulations are performed to show the effectiveness of the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권2호
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• pp.205-210
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• 2004

This paper deals with rule extraction from data using rough set theory. We construct the rule base in a hierarchical granulation structure by applying core as a classification criteria at each level. When more than one core exist, the coverage is used for the selection of an appropriate one among them to increase the classification rate and accuracy. In Addition, a probabilistic approach is suggested so that the partially useful information included in inconsistent data can be contributed to knowledge reduction in order to decrease the effect of the uncertainty or vagueness of data. As a result, the proposed method yields more proper and efficient rule base in compatability and size. The simulation result shows that it gives a good performance in spite of very simple rules and short conditionals.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 추계학술대회
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• pp.965-967
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• 2013

본 논문에서는 헬스케어시스템에서 효율적인 질병 관리를 위한 질병 규칙을 생성하는 질병규칙생성 기법(Disease Rule Creation Scheme)을 제안한다. DRCS는 기본적으로 Rough Set Theory의 개념을 이용하며, 각 속성과 결정 속성간의 지지율을 계산한다. 그리고 결정 속성을 기준으로 지지율이 낮은 속성은 제거한 후에, 질병을 판단할 수 있는 질병 규칙을 생성한다. 그 결과, DRCS는 C4.5알고리즘 보다 적은 질병규칙의 수를 생성하였으며, 질병 규칙의 정확성은 향상되었다.

• 정보보호학회논문지
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• 제30권3호
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• pp.481-491
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• 2020

악성 URL의 전송을 통한 악성코드 전파 및 불법적 정보 수집은 정보보안 분야의 가장 큰 위협 중의 하나이다. 특히, 최근 스마트폰의 보급으로 인하여 사용자들이 악성 URL에 노출될 확률이 더욱 높아지고 있다. 또한, 악성 URL을 노출 시키는 방법 역시 다양해 지고 있어서 이를 탐지하는 것이 점점 어려워지고 있다. 본 논문은 악성 URL에 대한 사용자의 경험에 대한 설문을 진행한 후, 이를 고려하여 악성 URL을 규칙기반으로 탐지하기 위한 라이브러리 개발 연구를 다루고 있다. 특히, 본 연구에서는 독자적인 규칙을 기반으로 악성 URL을 탐지 하기 위해 Rule-set을 정의하고, Rule-chain을 생성하여 악성 URL 탐지의 확장성을 제시하고 있다. 또한 어떤 애플리케이션에서도 활용이 가능한 라이브러리 형태로의 개발을 통해 다양한 응용프로그램에서 활용할 수 있도록 하였다.

• 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.