• Journal of Information Processing Systems
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• v.16 no.2
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• pp.360-376
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• 2020

Many heuristic attribute reduction algorithms have been proposed to find a single reduct that functions as the entire set of original attributes without loss of classification capability; however, the proposed reducts are not always perfect for these multiclass datasets. In this study, based on a probabilistic rough set model, we propose the class-oriented attribute reduction (COAR) algorithm, which separately finds a reduct for each target class. Thus, there is a strong dependence between a reduct and its target class. Consequently, we propose a type of ensemble constructed on a group of classifiers based on class-oriented reducts with a customized weighted majority voting strategy. We evaluated the performance of our proposed algorithm based on five real multiclass datasets. Experimental results confirm the superiority of the proposed method in terms of four general evaluation metrics.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.552-556
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• 2004

This paper surveys the design of the adaptive information filtering agents to retrieve the useful information within a large scale database. As the information retrieval through the Internet is generalized, it is necessary to extract the useful information satisfied the user's request condition to reduce the seeking time. For the first, this module is designed by the Rough reduct to generate the reduced minimal knowledge database considered the users natural query language in a large scale knowledge database, and also it is executed the soft computing by the fuzzy composite processing to operate the uncertain value of the reduced schema domain.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.479-484
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• 1998

입력된 영상으로부터 추론된 정보 표를 지식베이스에 저장하여 결정해를 구하는데는 많은 탐색시간이 소비된다. 본 논문에서는 탐색 시간을 감소시키기 위해서 러프집합의 식별(classification)과 근사(approximation) 개념을 이용하여 추론된 정보를 동치 클래스(equivalence class)로 분류하여 간략화한다. 감소된 규칙, 즉 Core와 Reduct 리스트를 구하여 결정해를 검색하는데 탐색 시간을 감소시키는데 있다.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.157-164
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• 2015

An efficient retrieval of useful information is a prerequisite of an optimal decision making system. Hence, A research of data mining techniques finding useful patterns from the various forms of data has been progressed with the increase of the application of Big Data for convergence and integration with other industries. Each technique is more likely to have its drawback so that the generalization of retrieving useful information is weak. Another integrated technique is essential for retrieving useful information. In this paper, a uncertainty measure of information is calculated such that algebraic probability is measured by Bayesian theory and then information entropy of the probability is measured. The proposed measure generates the effective reduct set (i.e., reduced set of necessary attributes) and formulating the core of the attribute set. Hence, the optimal decision rules are induced. Through simulation deciding contact lenses, the proposed approach is compared with the equivalence and value-reduct theories. As the result, the proposed is more general than the previous theories in useful decision-making.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.6
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• pp.683-690
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• 2015

In Rough set, attribute missing values have several problems such as reduct and core estimation. Further, they do not give some discernable pattern for decision tree construction. Now, there are several methods such as substitutions of typical attribute values, assignment of every possible value, event covering, C4.5 and special LEMS algorithm. However, they are mainly substitutions into frequently appearing values or common attribute ones. Thus, decision rules with high information loss are derived in case that important attribute values are missing in pattern matching. In particular, there is difficult to implement cross validation of the decision rules. In this paper we suggest new method for substituting the missing attribute values into high information gain by using entropy variation among given attributes, and thereby completing the information table. The suggested method is validated by conducting the same rough set analysis on the incomplete information system using the software ROSE.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.287-308
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• 2022

The explosive growth of cryptocurrency, led by Bitcoin has emerged as a major issue in the financial market recently. As a result, interest in cryptocurrency investment is increasing, but the market opens 24 hours and 365 days a year, price volatility, and exponentially increasing number of cryptocurrencies are provided as risks to cryptocurrency investors. For that reasons, It is raising the need for research to reduct investors' risks by dividing cryptocurrency which is not suitable for recommendation. Unlike the previous studies of maximizing returns by simply predicting the future of cryptocurrency prices or constructing cryptocurrency portfolios by focusing on returns, this paper reflects the tendencies of investors and presents an appropriate recommendation method with interpretation that can reduct investors' risks by selecting suitable Altcoins which are recommended using Apriori algorithm, one of the machine learning techniques, but based on the similarity and association rules of Bitocoin.

• 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.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.13 no.12
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• pp.1045-1050
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• 2000

Perdeuterated hexaflouroacetylacetonato-ytterbium [Yb(HFA-D)$_3$]complexes were synthesized by the keto-enol tautomerism reaction of Yb(HFA-H$_3$) in methanol-d$_4$in rder to reduct the radiationless transition to the ligands. The luminescence properties of Yb(HFA-D)$_3$complex were measured in the following anhydrous deuterated organic solvents ; Methanol-d$_4$, THF-d$_{8}$, PO(O$CH_3$)$_3$and DMSO-d$_{6}$. The intensity, lifetime and quantum efficiency of the luminescnce in DMSO-d$_{6}$ were superior to those in other deuterated solvents. It was suggested that the anhydrous DMSO-d$_{6}$ might be the most appropriate solvent for the liquid laser material of Yb(HFA-D)$_3$complex.complex.

• 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.

• 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.