• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.668-671
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• 1997

This paper describes a new process-plan selection method using a modified Fuzzy Quantification Theory(FQT). The problem for process-plan selection can be characterized by multiple attributes and used subjective, uncertain information. Fuzzy Quantification Theory is used for handling such informations because it is a useful tool when human judgment or evaluation is quantified via linguistic variables and the proposed method is concerned with the selection of a process plan by derivation of the values of categories for each attribute. In this paper, a modified Fuzzy Quantification Theory(FQT) is described and the procedure of this approach is explained and examples are illustrated.

• 컴퓨터교육학회논문지
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• 제6권3호
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• pp.65-73
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• 2003

대부분의 웹 기반 학습은 학습자에게 획일적이고 일관된 학습자료를 제공해주고 있다. 이에 문항분석방법을 적용하거나 학습패턴을 이용하여 웹 기반 학습 시스템에 도입하려는 연구가 진행되었다. 그러나, 이러한 방법을 적용하기 위해서는 복잡한 알고리즘이나 파라미터 설정등이 필요하다. 따라서, 본 연구에서는 문항출제특성 중 문항에 대한 학습자의 오답율과 출제 빈도수를 고려하여 문제를 추출하는 시스템을 설계 및 구현하였다. 또한, 문제 추출에 관한 비즈니스 로직을 EJB로 구현함으로서, 효율적인 시스템 개발이 가능하였으며 유지보수 및 재사용성을 높였다.

• Journal of Computing Science and Engineering
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• 제11권4호
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• pp.130-141
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• 2017

Feature selection is one of the most challenging problems of pattern recognition and data mining. In this paper, a feature selection algorithm based on an improved version of binary differential evolution is proposed. The method simultaneously optimizes two feature selection criteria, namely, set approximation accuracy of rough set theory and relational algebra based derived score, in order to select the most relevant feature subset from an entire feature set. Superiority of the proposed method over other state-of-the-art methods is confirmed by experimental results, which is conducted over seven publicly available benchmark datasets of different characteristics such as a low number of objects with a high number of features, and a high number of objects with a low number of features.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.879-894
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• 2003

We evaluated the efficiencies of applying attribute selection methods and prior discretization to supervised learning, modelled by C4.5 and Naive Bayes. Three databases were obtained from UCI data archive, which consisted of continuous attributes except for one decision attribute. Four methods were used for attribute selection : MDI, ReliefF, Gain Ratio and Consistency-based method. MDI and ReliefF can be used for both continuous and discrete attributes, but the other two methods can be used only for discrete attributes. Discretization was performed using the Fayyad and Irani method. To investigate the effect of noise included in the database, noises were introduced into the data sets up to the extents of 10 or 20%, and then the data, including those either containing the noises or not, were processed through the steps of attribute selection, discretization and classification. The results of this study indicate that classification of the data based on selected attributes yields higher accuracy than in the case of classifying the full data set, and prior discretization does not lower the accuracy.

• 제어로봇시스템학회논문지
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• 제3권5호
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• pp.490-496
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• 1997

This paper describes a new process-plan selection method using a modified Fuzzy Quantification Theory(FQT). The problem of process-plan selection can be characterized by multiple attributes and used subjective, uncertain information. Fuzzy Quantification Theory is used for handling such information because it is a useful tool when human judgment or evaluation is quantified via linguistic variables, and the proposed method is concerned with the selection of a process plan by derivation of the values of categories for each attribute. In this paper, a modified Fuzzy Quantification Theory(FQT) is described and the procedure of this approach is explained and examples illustrated.

• Journal of the Korean Data and Information Science Society
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• 제23권1호
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• pp.199-207
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• 2012

Recently, variable selection methods using penalized likelihood with a shrink penalty function have been widely studied in various statistical models including generalized linear models and survival models. In particular, they select important variables and estimate coefficients of covariates simultaneously. In this paper, we develop a penalize h-likelihood method for variable selection in gamma frailty models. For this we use the smoothly clipped absolute deviation (SCAD) penalty function, which satisfies a good property in variable selection. The proposed method is illustrated using simulation study and a practical data set.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.1006-1010
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• 1995

This paper describes Sexual Reproduction Genetic Algorithm(SRGA) for function optimization. In SRGA, each individual utilize a diploid chromosome structure. Sex cells(gametes) are produced through artificial meiosis in which crossover and mutation occur. The proposed method has two selection operators, one, individual selection which selects the individual to fertilize, and the other, gamete selection which makes zygote for offspring production. We consider the effects of multi-selection and diploidy on search performance. SRGA improves local and global search(exploitation and exploration) and show optimum tracking performance in nonstationary environments. Gray coding is incorporated to transforming the search space and Genic uniform distribution method is proposed to alleviate the problem of premature convergence.

• 한국국방경영분석학회지
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• 제25권1호
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• pp.75-86
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• 1999

The selection criteria of an Authorized Stockage List (ASL) in the Army is based on Army Regulation(AR)409. However, the current selection method of ASL is not considered in cost, weight and volume of repair parts. This paper is focused on developing for a new selection model taking account of cost, weight and volume of repair parts. Goal programming is utilized in order to consider weighted priorities. Different units of cost, and volume are normalized for using weighing value. Real data of a field division are applied to the model. Results of the new selection model are more reduced in cost, weight and volume than those of the previous method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권4호
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• pp.277-281
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• 2006

Cyber counseling, one of the most compatible type of consultation for the information society, enables people to reveal their mental agonies and private problems anonymously, since it does not require face-to-face interview between a counsellor and a client. However, there are few cyber counseling centers which provide high quality and trustworthy service, although the number of cyber counseling center has highly increased. Therefore, this paper is intended to enable an appropriate consultation for each client by analyzing client propensity using Bayesian variable selection. Bayesian variable selection is superior to stepwise regression analysis method in finding out a regression model. Stepwise regression analysis method, which has been generally used to analyze individual propensity in linear regression model, is not efficient since it is hard to select a proper model for its own defects. In this paper, based on the case database of current cyber counseling centers in the web, we will analyze clients' propensities using Bayesian variable selection to enable individually target counseling and to activate cyber counseling programs.

• 한국컴퓨터정보학회논문지
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• 제23권9호
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• pp.7-13
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• 2018

Recent advancement in data gathering technique improves the capability of information collecting, thus allowing the learning process between gathered data patterns and application sub-tasks. A pattern can be associated with multiple labels, demanding multi-label learning capability, resulting in significant attention to multi-label feature selection since it can improve multi-label learning accuracy. However, existing evolutionary multi-label feature selection methods suffer from ineffective search process. In this study, we propose a evolutionary search process for the task of multi-label feature selection problem. The proposed method creates large set of offspring or new feature subsets and then retains the most promising feature subset. Experimental results demonstrate that the proposed method can identify feature subsets giving good multi-label classification accuracy much faster than conventional methods.