• 한국지능시스템학회논문지
• /
• 제11권7호
• /
• pp.666-674
• /
• 2001

Classical methods to find a minimal set of rules based on the rough set theory are known to be ineffective in dealing with new instances added to the universe. This paper introduces an inductive learning algorithm for incrementally retrieving a minimal set of rules from a given decision table. Then, the algorithm is validated via simulations with two sets of data, in comparison with a classical non-incremental algorithm. The simulation results show that the proposed algorithm is effective in dealing with new instances, especially in practical use.

• Journal of applied mathematics & informatics
• /
• 제30권5_6호
• /
• pp.1023-1030
• /
• 2012

Rough sets and rough filter in BE-algebras are established an some related their properties are discussed.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
• /
• pp.248-251
• /
• 1997

In this paper, We propose the concept of approximate Classification in the field of two group discriminan analysis. In our approach, an attribute space is divided into three subspaces. Two subspaces are for given two group and one subspace is for a boundary area between the two groups. We propose Approximate Pattern Classification with Rough set. We also propose learning procedures of neural networks for approximate classification. We propose two weighting methods which lead to possibility analysis and necessity analysis. We illustrate the proposed methods by numerical examples.

• Korean Journal of Mathematics
• /
• 제29권1호
• /
• pp.81-89
• /
• 2021

In this paper, we introduce the notions of a distance function, Alexandrov topology and ⊖-upper (⊕-lower) approximation operator based on complete co-residuated lattices. Under various relations, we define (⊕, ⊖)-fuzzy rough set on complete co-residuated lattices. Moreover, we study their properties and give their examples.

• 대한수학회논문집
• /
• 제31권3호
• /
• pp.451-460
• /
• 2016

The notion of a rough fuzzy filter in a BE-algebra is introduced and some properties of such a filter are investigated. The relations between the upper (lower) rough filters and the upper (lower) approximations of their homomorphism images are discussed.

• 호남수학학술지
• /
• 제43권1호
• /
• pp.47-58
• /
• 2021

In this paper, we introduce rough statistical convergence of difference double sequences in normed linear spaces as an extension of rough convergence. We define the set of rough statistical limit points of a difference double sequence and analyze the results with proofs.

• 한국정보처리학회논문지
• /
• 제6권8호
• /
• pp.2106-2112
• /
• 1999

의사결정에 있어 정보란 의사결정자가 의사결정을 하는데 사용하도록 의미 있고 유용한 형태로 처리된 데이터이다. 이러한 정보들에 있어서 불필요한 속성들을 제거하여 처리함으로써 의사결정의 효율을 높일 수 있다. 러프 집합 이론은 불필요한 속성을 제거하고 분류화 하는데 뛰어난 능력을 가지고 있으나 속성 감축시 속성 수와 튜플 수에 따라 복잡한 계산을 요구한다. 따라서 속성들 사이의 상호연관성을 나타내는 척도로서, 두 변수간의 독립성에 관한 검정방법인 $\chi$2와 러프 집합의 종속성을 이용하여 속성들을 감축하고 이를 신경망의 입력 유니트로 사용하므로써 기존의 모든 속성을 입력으로 하여 신경망을 구성하는 것보다 간단하며 학습효율의 향상 및 정확한 의사결정을 지원할 수 있다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
• /
• pp.333-336
• /
• 2002

In many cases, fuzzy modeling has a defect that the design procedure cannot be theoretically justified. To overcome this difficulty, we suggest a new design method for fuzzy model by combining genetic algorithm(GA) and mush set theory. GA, which has the advantages is optimization, and rule base. However, it is some what time consuming, so are introduce rough set theory to the rule reduction procedure. As a result, the decrease of learning time and the considerable rate of rule reduction is achieved without loss of useful information. The preposed algorithm is composed of three stages; First stage is quasi-optimization of fuzzy model using GA(coarse tuning). Next the obtained rule base is reduced by rough set concept(rule reduction). Finally we perform re-optimization of the membership functions by GA(fine tuning). To check the effectiveness of the suggested algorithm, examples for time series prediction are examined.

• Journal of applied mathematics & informatics
• /
• 제38권3_4호
• /
• pp.201-209
• /
• 2020

We introduce and study some basic properties of rough I-convergent of triple sequence spaces and also study the set of all rough I-limits of a triple sequence spaces.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제15권3호
• /
• pp.208-215
• /
• 2015

Since upper and lower approximations could be induced from the rough set structures, rough sets are considered as approximations. The concept of fuzzy rough sets was proposed by replacing crisp binary relations with fuzzy relations by Dubois and Prade. In this paper, we introduce and investigate some properties of intuitionistic fuzzy rough approximation operators and intuitionistic fuzzy relations by means of topology.