• 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.

• 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.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.51-63
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• 2003

Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. There are several treatments to deal with the incomplete data problem such as case deletion and single imputation. Those approaches are simple and easy to implement but they may provide biased results. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.

• International Journal of Advanced Culture Technology
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• v.7 no.3
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• pp.92-96
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• 2019

This study analyzes the problems of fire qualification information websites and job search websites due to incomplete information in the job search process and suggests an improvement plan. It has been confirmed that the main reason for the cost of job searching is incomplete information required for a job search and job search through existing analysis. As a result, it is suggested to construct a smooth information system for economic entities and to provide easy access to information by mitigating the incompleteness of information. Based on this, analysis of the problems of Korean qualifications in the firefighting realm reveals that there is a qualification holder information and a job information site, and a qualification holder management system is established but only information of either qualification acquisition information or employment information is provided. In addition, it is easy to access information through a qualification acquisition information and employment information site via the Internet, but there are inconveniences that qualification acquisition information and employment information are dualized. In order to improve this, it is necessary to build a new customized integrated qualification management system that covers existing Q-net qualification acquisition information and worknet employment information.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data(a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology for sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.121-127
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• 2013

When input power is small, nonideal symmetric balance incomplete block design(BIBD) code design is effective in the spectral-amplitude-code(SAC) optical code-division multiple-access (OCDMA) system. But the number of simultaneous users is not sufficient. To overcome this property, we propose 2-D BIBD codes, where nonideal BIBD codes are used as spectral code and ideal BIBD codes are used as spatial codes. Analytical results show that the number of simultaneous users increases significantly.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.105-110
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• 2003

Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference. data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values.. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.

• The KIPS Transactions:PartC
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• v.18C no.3
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• pp.127-134
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• 2011

Malicious botnet has been used for more malicious activities, such as DDoS attacks, sending spam messages, steal personal information, etc. To prevent this, many studies have been preceded. But malicious botnets have evolved and evaded detection systems. In particular, HTTP GET Request attack that exploits the vulnerability of the application layer is used. ALAB of ALADDIN proposed by ETRI is DDoS attack detection system that HTTP GET, Incomplete GET request flooding attack detection algorithm is applied. In this paper, we extend Incomplete GET detection algorithm of ALAB and derive the optimal configuration parameters to verify the validity of the algorithm ALAB by the study of the normal and attack packets.