• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.441-447
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• 2001

In general, Rough Set theory is used for classification, inference, and decision analysis of incomplete data by using approximation space concepts in information system. Information system can include quantitative attribute values which have interval characteristics, or incomplete data such as multiple or unknown(missing) data. These incomplete data cause tole inconsistency in information system and decrease the classification ability in system using Rough Sets. In this paper, we present various types of incomplete data which may occur in information system and propose INcomplete information Processing System(INiPS) which converts incomplete information system into complete information system in using Rough Sets.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.243-251
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• 2000

In general, Rough Set theory is used for classification, inference, and decision analysis of incomplete data by using approximation space concepts in information system. Information system can include quantitative attribute values which have interval characteristics, or incomplete data such as multiple or unknown(missing) data. These incomplete data cause the inconsistency in information system and decrease the classification ability in system using Rough Sets. In this paper, we present various types of incomplete data which may occur in information system and propose INcomplete information Processing System(INiPS) which converts incomplete information system into complete information system in using Rough Sets.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.5
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• pp.214-219
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• 2006

Oligopoly differs from perfect competition and monopoly in that a firm must consider rival firms' behavior to determine its own best policy. This interrelationship among firms is the issue examined in this paper. In the oligopoly market, the complete information market means that each producer has full information about itself, the market, and its rivals. That is, each producer knows the market demand function, its own cost function and the cost functions of rivals. On the other hand, the incomplete information market means that in general each producer lacks full information about the market or its rivals. Here, we assume that each firm doesn't know the cost functions and the strategic biddings of its rivals. The main purpose of this paper is to analyze firm' strategic behaviors and equilibrium in an electricity market with incomplete information. In the case study, the complete information market and the incomplete market are compared at the Nash Equilibrium from the viewpoints of market price, transaction quantities, consumer benefits, and Social Welfare.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.361-362
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• 2006

This paper presents the result of survey and analysis on a theoretical approach to the application of incomplete information game in deregulated Power Pools. The deregulation power market arc modeled by the incomplete information game. The case where participants have incomplete information about the operation costs of other participants are highlighted. Pool participants define transactions to maximize their benefit in non-cooperative situation, the ISO defines transactions among participants by looking for minimum price that satisfies the demand in the Pool. The incomplete information game determines Nash equilibrium satisfied Pool participants and the ISO.

• Korean Management Science Review
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• v.32 no.1
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• pp.15-33
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• 2015

Under complete information, introducing additional constraints to a portfolio will have a negative impact on performance. However, real-life investments inevitably involve use of error-prone estimations, such as expected stock returns. In addition to the reality of incomplete data, investments of most Korean domestic equity funds are regulated externally by the government, as well as internally, resulting in limited maximum investment allocation to single stocks and risk free assets. This paper presents an investment framework, which takes such real-life situations into account, based on a newly developed portfolio selection model considering realistic constraints under incomplete information. Additionally, we examined the effects of additional constraints on portfolio's performance under incomplete information, taking the well-known Samsung and SK group stocks as performance benchmarks during the period beginning from the launch of each commercial fund, 2005 and 2007 respectively, up to 2013. The empirical study shows that an investment model, built under incomplete information with additional constraints, outperformed a model built without any constraints, and benchmarks, in terms of rate of return, standard deviation of returns, and Sharpe ratio.

• Journal of Korea Multimedia Society
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• v.3 no.5
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• pp.550-556
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• 2000

This paper comes to derive optimal decision rule from incomplete information using the concept of indiscernibility relation and approximation space in Rough set. As there may be some errors in case that processing information contains multiple or missing data, the method of removing or minimizing these data is required. Entropy which is used to measure uncertainty or quantity in information processing field is utilized to remove the incomplete information of rough relation database. But this paper does not always deal with the information system which may be contained incomplete information. This paper is proposed object relation entropy and attribute relation entropy using Rough set as information theoretical measures in order to remove the incomplete information which may contain condition attribute and decision attribute of information system.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.778-780
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• 2005

Electric power industry throughout the world is restructured. The electric power industry has a characteristics of an oligopoly with an imperfect competition. In Korea rules, all information is not available. So the strategy under such incomplete information market differ firm those under complete information system in game theory. This paper presents a analysis technique if Korea ma rket model with incomplete information.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.5
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• pp.848-853
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• 2007

This paper presents a methodology of estimating incomplete information in electricity markets for analyzing the gaming behavior of Generating Companies (GENCOs). Each GENCO needs to model its opponents' unknown information of strategic biddings and cost functions. In electricity markets with complete information, each GENCO knows its rivals' payoff functions and tries to maximize its own profit at Nash equilibriurnl Nli) by acknowledging the rivals' cost function. On the other hand, in the incomplete information markets, each GENCO lacks information about its rivals. Load patterns can change continuously due to many factors such as weather, price, contingency, etc. In this paper, we propose the method of the estimation of the opponents' cost function using market price, transaction quantities. and customer load patterns. A numerical example with two GENCOs is illustrated to show the basic idea and effectiveness of the proposed methodology.

• Journal of information and communication convergence engineering
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• v.8 no.4
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• pp.383-386
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• 2010

Pattern classification is one of the most important topics for machine learning research fields. However incomplete data appear frequently in real world problems and also show low learning rate in classification models. There have been many researches for handling such incomplete data, but most of the researches are focusing on training stages. In this paper, we proposed two classification methods for incomplete data using triangular shaped fuzzy membership functions. In the proposed methods, missing data in incomplete feature vectors are inferred, learned and applied to the proposed classifier using triangular shaped fuzzy membership functions. In the experiment, we verified that the proposed methods show higher classification rate than a conventional method.

• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.139-145
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• 2002

This paper deals with the problem of obtaining the Bayesian predictive density function and the prediction intervals for a future observation and the p-th order statistics of n future observations for the exponential model under the censored sampling with incomplete information.