• The KIPS Transactions:PartD
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• v.8D no.3
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• pp.203-210
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• 2001

These days data is being collected and accumulated in a wide variety of fields. Stored data itself is to be an information system which helps us to make decisions. An information system includes many kinds of necessary and unnecessary attribute. So many algorithms have been developed for finding useful patterns from the data and reasoning approximately new objects. We are interested in the simple and understandable rules that can represent useful patterns. In this paper we propose an algorithm which can reduce the information in the system to a minimum, based on a probabilistic rough set theory. The proposed algorithm uses a value that tolerates accuracy of classification. The tolerant value helps minimizing the necessary attribute which is needed to reason a new object by reducing conditional attributes. It has the advantage that it reduces the time of generalizing rules. We experiment a proposed algorithm with the IRIS data and Wisconsin Breast Cancer data. The experiment results show that this algorithm retrieves a small reduct, and minimizes the size of the rule under the tolerant classification rate.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.343-349
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• 2013

Rough set theory comes to derive optimal rules through the effective selection of features from the redundancy of lots of information in data mining using the concept of equivalence relation and approximation space in rough set. The reduction of attributes is one of the most important parts in its applications of rough set. This paper purports to define a information-theoretic measure for determining the most important attribute within the association of attributes using rough entropy. The proposed method generates the effective reduct set and formulates the core of the attribute set through the elimination of the redundant attributes. Subsequently, the control rules are generated with a subset of feature which retain the accuracy of the original features through the reduction.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.37-40
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• 2001

본 논문에서는 전문가 시스템에서 지식에 대한 규칙을 감소시키기 위해 러프 집합을 이용한 지식 감축 방법을 제안한다. 또한, 속성 항을 클래스로 분류하여 각 클래스와 이웃하는 클래스의 항들을 비교하여 리덕트와 코어를 구하여 최소화하였다. 이러한 방법은 방대한 양의 규칙을 최소화함으로써 의사결정 시간을 단축시킬 수 있다.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.223-229
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• 2014

In an attempt to retrieve useful information for an efficient decision in the large knowledge system, it is generally necessary and important for a refined feature selection. Rough set has difficulty in generating optimal reducts and classifying boundary objects. In this paper, we propose quick reduction algorithm generating optimal features by rough entropy analysis for condition and decision attributes to improve these restrictions. We define a new conditional information entropy for efficient feature extraction and describe procedure of feature selection to classify the significance of features. Through the simulation of 5 datasets from UCI storage, we compare our feature selection approach based on rough set theory with the other selection theories. As the result, our modeling method is more efficient than the previous theories in classification accuracy for feature selection.

• Resources Recycling
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• v.26 no.5
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• pp.48-53
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• 2017

During steelmaking on EAF, 1 ~ 2% of dust is generated. EAF Dust contains 20 ~ 30% of Zn and Fe. Dust contained in Off-gas is passed through combustion tower and cooling tower, and then captured in bag filter. About 15 wt.% of dust is dropped at the bottom of Combustion tower by its specific gravity, which was also carried out to recycle company with more higher charge than Bag filter dust. This study is focused on the combustion tower dust, and seperation as a function of operation period and particle size. As a result, Zn and Fe content of dust is more affected by size factor than operation period.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.135-142
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• 2009

Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. The major techniques used in data mining are mining association rules, classification and clustering. Since these techniques are used individually, it is necessary to develop the methodology for rule extraction using a process of integrating these techniques. Rule extraction techniques assist humans in analyzing of large data sets and to turn the meaningful information contained in the data sets into successful decision making. This paper proposes an autonomous method of rule extraction using clustering and rough set theory. The experiments are carried out on data sets of UCI KDD archive and present decision rules from the proposed method. These rules can be successfully used for making decisions.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.293-301
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• 2014

In this paper, we propose a new measure based on the confidence of Bayesian posterior probability so as to reduce unimportant information in the clustering process. Because the performance of clustering is up to selecting the important degree of attributes within the databases, the concept of information entropy is added to posterior probability for attributes discernibility. Hence, The same value of attributes in the confidence of the proposed measure is considerably much less due to the natural logarithm. Therefore posterior probability-based clustering algorithm selects the minimum of attribute reducts and improves the efficiency of clustering. Analysis of the validation of the proposed algorithms compared with others shows their discernibility as well as ability of clustering to handle uncertainty with ACME categorical data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.69-74
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• 2015

In this paper the patterns of information system is reduced so that control rules can guarantee fast response of queries in database. Generally an information system includes many kinds of necessary and unnecessary attribute. In particular, inconsistent information system is less likely to acquire the accuracy of response. Hence we are interested in the simple and understandable rules that can represent useful patterns by means of rough entropy and Bayesian posterior probability. We propose an algorithm which can reduce control rules to a minimum without inadequate patterns such that the implication between condition attributes and decision attributes is measured through the framework of rough entropy. Subsequently the validation of the proposed algorithm is showed through test information system of new employees appointment.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.5
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• pp.535-542
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• 2015

A reactor containment acts as a final barrier to prevent leakage of radioactive material due to the possible reactor accidents into external environment. Because of the functional importance of the containment building, the SIT(Structural Integrity Test) for containments shall be performed to evaluate the structural acceptability and demonstrate the quality of construction. In this paper, numerical analyses are presented, which simulate the results obtained from the SIT for a prestressed concrete(PSC) structure. A sophisticate structural analysis model is developed to simulate the structural behavior during the SIT properly based on various preliminary analysis results considering contact condition among structural elements. From the comparison of the analysis and test results based on the acceptance criteria of ASME CC-6000, it can be concluded that the construction quality of the containment has been well maintained and the acceptable performance of new design features has been verified.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.5
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• pp.523-533
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• 2015

A reactor containment acts as a final barrier to prevent leakage of radioactive material due to the possible reactor accidents into external environment. Because of the functional importance of the containment building, the SIT(Structural Integrity Test) for containments shall be performed to evaluate the structural acceptability and demonstrate the quality of construction. An initial numerical analysis was performed to simulate the results obtained from the SIT for a prestressed concrete(PSC) structure. But the analysis results by the initial model expected smaller displacements than the measured ones by 30% at some locations. Accordingly, the research and development to improve the initial model to corelate the measured results of the SIT more properly have been performed. In this paper, the effects of the loss of concrete due to duct for tendons and the contact of duct and tendons in un-bonded tendon system are mainly evaluated based on the preliminary analysis results. In addition, the importances of the proper definition of mesh connectivity among structural elements of concrete, liner plates, rebars and tendons are discussed.