• Journal of the Korean Society for Library and Information Science
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• v.47 no.1
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• pp.419-448
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• 2013

This study is to describe the principles and outcome of the development for a classification scheme of presidential records to manage efficiently and provide them as archival information service productively. First, this scheme accommodates a management aspect, taking consideration into the aspect of archival records as the results of government administrative duties. Additionally, governance aspects are embraced into this scheme since presidential records can be seen as the results of governing purpose. We are considering the functional aspects of the public records too. As a result, this scheme adopts both management and governance aspect. Focusing on reflecting both functional and governance aspects, first analyzes existing classification systems in domestic and abroad. Finally, this study proposes 24 first-level, 114 second-level, and 179 third-level classification categories. Archivists and classification experts examined the classification scheme for verification by advisory meetings.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.876-883
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• 2008

A clearly defined e-catalog (or product) information is a key foundation for an e-commerce system. A classification (or categorization) is a core information to build clear e-catalogs, can play an important role in quality of e-commerce systems using e-catalogs. However, as the wide use of online business transactions, the volume of e-catalog information that needs to be managed in a system has become drastically large, and the classification task of such data has become highly complex. In this paper, we present an e-catalog classifier system, and report on our effort to improve an e-catalog management process and to standardize e-catalogs for enterprises by use of automated approach for e-catalog classifier systems. Also we introduce some of the issues that we have experienced in the projects, so that our work may help those who do a similar project in the future.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.203-214
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• 2023

The Korea Customs Service is efficiently handling business with an electronic customs system that can effectively handle one-stop business. This is the case and a more effective method is needed. Import and export require HS Code (Harmonized System Code) for classification and tax rate application for all goods, and item classification that classifies the HS Code is a highly difficult task that requires specialized knowledge and experience and is an important part of customs clearance procedures. Therefore, this study uses various types of data information such as product name, product description, and product image in the item classification request form to learn and develop a deep learning model to reflect information well based on Multimodal representation learning. It is expected to reduce the burden of customs duties by classifying and recommending HS Codes and help with customs procedures by promptly classifying items.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2785-2791
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• 2012

In this paper, a method for automatic classification of learning objects is proposed for effective management and reuse of learning contents. Proposed method will create cohesion of learning objects using cases of learning objects and perform automatic classification of learning objects by measuring their relationship based on cohesion. Application of proposed method to learning management system has the advantage of reducing the costs for developing learning contents. Simulation has shown the average accuracy of 28.20% with probability-based method and 56.38% with cohesion-based method. Simulation has proved that the method proposed in this paper is effective in automatic classification of learning objects.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1161-1167
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• 2018

In order to cope with dramatically increasing malware variant, malware classification research is getting diversified. Recent research tend to grasp individual limits of existing malware analysis technology (static/dynamic), and to change each method into "hybrid analysis", which is to mix different methods into one. Futhermore, it is applying machine learning to identify malware variant more accurately, which are difficult to classify. However, accuracy and scalability of trade-off problems that occur when using all kinds of methods are not yet to be solved, and it is still an important issue in the field of malware research. Therefore, to supplement and to solve the problems of the original malware classification research, we are focusing on developing a new malware classification system in this research.

• Journal of Internet Computing and Services
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• v.1 no.1
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• pp.63-72
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• 2000

Data mining means a process of nontrivial extraction of hidden knowledge or potentially useful information from data in large databases. Data mining algorithm is a multi-disciplinary field of research; machine learning, statistics, and computer science all make a contribution. Different classification schemes can be used to categorize data mining methods based on the kinds of tasks to be implemented and the kinds of application classes to be utilized, and classification has been identified as an important task in the emerging field of data mining. Since classification is the basic element of human's way of thinking, it is a well-studied problem in a wide varietyof application. In this paper, we propose a classifier system based on genetic algorithm with robust property, and the proposed system is evaluated by applying it to nDmC problem related to classification task in data mining.

• Journal of Korean Library and Information Science Society
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• v.39 no.4
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• pp.189-214
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• 2008

Objective of this research is to point out problems of geographic division structure in current Korean Decimal Classification System and provide solutions. For this purpose key classification methods were divided to decimal and non-decimal classification methods and analyzed for geographical division principles. In addition, national institutes regional division standards from Korea, USA and Japan were researched. Through these analysis, we provided suggestions to improve the table of geographical division in KDC4 including public institutions administrative district classification structure relations and consistency, and other regional divisional standards (proposal) instead of typical administrative district reflecting various geographical conditions.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.3
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• pp.85-93
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• 2015

This paper mainly introduces the methods of extracting landslide information using ALOS(Advanced Land Observing Satellite) images and GIS(Geographical Information System) technology. In this study, we classified images using three different methods which are the unsupervised the supervised and the PCA(Principal Components Analysis) for extracting landslide information based on characteristics of ALOS image. From the image classification results, we found out that the quality of classified image extracted with PCA supervised method was superior than the other images extracted with the other methods. But the accuracy of landslide information extracted from this image classification was still very low as the pixels were very similar between the landslide and safety regions. It means that it is really difficult to distinguish those areas with an image classification method alone because the values of pixels between the landslide and other areas were similar, particularly in a region where the landslide and other areas coexist. To solve this problem, we used the LSM(Landslide Susceptibility Map) created with ArcView software through weighted overlay GIS method in the areas. Finally, the developed LSM was applied to the image classification process using the ALOS images. The accuracy of the extracted landslide information was improved after adopting the PCA and LSM methods. Finally, we found that the landslide region in the study area can be calculated and the accuracy can also be improved with the LSM and PCA image classification methods using GIS tools.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.422-432
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• 2019

A hybrid kernel function of support vector machine is proposed to improve the classification performance of power quality disturbances. The kernel function mathematical model of support vector machine directly affects the classification performance. Different types of kernel functions have different generalization ability and learning ability. The single kernel function cannot have better ability both in learning and generalization. To overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to improve both the ability in generation and learning. In simulations, we respectively used the single and multiple power quality disturbances to test classification performance of support vector machine algorithm with the proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved support vector machine algorithm has better performance for the classification of power quality signals with single and multiple disturbances.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1442-1462
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• 2014

Packet classification is a key technology of the Internet for routers to classify the arriving packets into different flows according to the predefined rulesets. Previous packet classification algorithms have mainly focused on search speed and memory usage, while overlooking update performance. In this paper, we propose PreCuts, which can drastically improve the update speed. According to the characteristics of IP field, we implement three heuristics to build a 3-layer decision tree. In the first layer, we group the rules with the same highest byte of source and destination IP addresses. For the second layer, we cluster the rules which share the same IP prefix length. Finally, we use the heuristic of information entropy-based bit partition to choose some specific bits of IP prefix to split the ruleset into subsets. The heuristics of PreCuts will not introduce rule duplication and incremental update will not reduce the time and space performance. Using ClassBench, it is shown that compared with BRPS and EffiCuts, the proposed algorithm not only improves the time and space performance, but also greatly increases the update speed.