• The Journal of the Korea Contents Association
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• v.18 no.10
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• pp.443-452
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• 2018

With the explosively increasing intellectual property rights, securing technological competitiveness of companies is more and more important. In particular, since patents include core technologies and element technologies, patent analysis researches are actively conducted to measure the technological value of companies. Various patent analysis studies have been conducted by the International Patent Classification(IPC), which does not include the latest technical classification, and the technical classification accuracy is low. In order to overcome this problem, the Cooperative Patent Classification(CPC), which includes the latest technology classification and detailed technical classification, has been developed. In this paper, we propose a model to analyze the classification of the technologies included in the patent by using the detailed classification system of CPC. It is possible to analyze the inventor's patents in consideration of the relation, importance, and efficiency between the detailed classification schemes of the CPCs to extract the core technology fields and to analyze the details more accurately than the existing IPC-based methods. Also, we perform the comparative evaluation with the existing IPC based patent analysis method and confirm that the proposed model shows better performance in analyzing the inventor's core technology classification.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.2
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• pp.47-56
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• 2014

Internet traffic has rapidly increased due to the supplying wireless devices and the appearance of various applications and services. By increasing internet traffic rapidly, the need of Internet traffic classification becomes important for the effective use of network resource. However, the traffic classification scheme is not much studied comparing to the study for classification method. This paper proposes novel classification scheme for multilateral and hierarchical traffic identification. The proposed scheme can support multilateral identification with 4 classification criteria such as service, application, protocol, and function. In addition, the proposed scheme can support hierarchical analysis based on roll-up and drill-down operation. We prove the applicability and advantages of the proposed scheme by applying it to real campus network traffic.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.1
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• pp.57-79
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• 2013

The aim of this study is to reveal the categorizational and classificatory features of the subject-based classification (SBC) as a subject organization system. For this purpose, 12 SBC schemes of public libraries were selected and a comparative analysis was made between the traditional classification system, such as DDC and SBC in terms of the categorizational aspects, and canons for the classification. As a result, there were significant and considerable differences between the two types of classifications. This study concluded that SBC cannot be clearly explained and understood without a consideration of its essential and distinctive characteristics as a classification scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1287-1294
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• 2010

The traffic classification is a preliminary and essential step for stable network service provision and efficient network resource management. While a number of classification methods have been introduced in literature, the payload signature-based classification method shows the highest performance in terms of accuracy, completeness, and practicality. However, the payload signature-based method has a significant drawback in high-speed network environment that the processing speed is much slower than other classification method such as header-based and statistical methods. In this paper, We describes various design options to improve the processing speed of traffic classification in design of a payload signature based classification system and describes our selections on the development of our traffic classification system. Also the feasibility of our selection was proved through experimental evaluation on our campus traffic trace.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.587-598
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• 2006

The number of variables exceeds the number of samples in microarray data. We propose a nonparametric local linear logistic classification procedure using orthogonal components for classifying high-dimensional microarray data. The proposed method is based on the local likelihood and can be applied to multi-class classification. We applied the local linear logistic classification method using PCA, PLS, and factor analysis components as new features to Leukemia data and colon data, and compare the performance of the proposed method with the conventional statistical classification procedures. The proposed method outperforms the conventional ones for each component, and PLS has shown best performance when it is embedded in the proposed method among the three orthogonal components.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.89-96
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• 2007

Various classification methods have been developed to reuse components. These classification methods enable the user to access the needed components quickly and easily. Conventional classification approaches include the following problems: a labor-intensive domain analysis effort to build a classification structure, the representation of the inter-component relationships, difficult to maintain as the domain evolves, and applied to a limited domain. In order to solve these problems, this paper describes a composite cluster analysis approach for component classification. The cluster analysis approach is a combination of a hierarchical cluster analysis method, which generates a stable clustering structure automatically, and a non-hierarchical cluster analysis concept, which classifies new components automatically. The clustering information generated from the proposed approach can support the domain analysis process.

• Journal of the Korean Society for Library and Information Science
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• v.41 no.3
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• pp.127-144
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• 2007

The purpose of this study is to design the classification system for christian information resources on the internet in Korea. For this purpose, the study is investigated the divisions of Christianity of (1) library classifications: KDC, DDC, LCC, (2) portal sites: Daum, Empas, Naver, (3) Christianity Portal sites GodPeaple, Kidok, Godpia. And it compared the classification systems of KDC, DDC and GodPeaple. This study selected criteria as follows: comprehension, logicality, definiteness, efficiency and current topics. It suggested the classification system(draft) for christian information resources on internet which are composed of 10 classes.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.2 s.16
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• pp.23-33
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• 2000

On this study, Among the classification methods of land cover using satellite imagery, we compared the classification accuracy of Neural Network Classifier and that of Maximum Likelihood Classifier which has the characteristics of parametric and non-parametric classification method. In the assessment of classification accuracy, we analyzed the classification accuracy about testing area as well as training area that many analysts use generally when assess the classification accuracy. As a result, Neural Network Classifier is superior to Maximum Likelihood Classifier as much as 3% in the classification of training data. When ground reference data is used, we could get poor result from both of classification methods, but we could reach conclusion that the classification result of Neural Network Classifier is superior to the classification result of Maximum Likelihood Classifier as much as 10%.

• Spatial Information Research
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• v.19 no.6
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• pp.43-54
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• 2011

This study proposes a rule-based image classification method for the time-series analysis of changes in the land surface of the Seongnam-Yongin area using satellite-image data from 2000 to 2009. In order to identify the change patterns during each period, 11 classes were employed in accordance with statistical/mathematic rules. A generalized algorithm was used so that the rules could be applied to the unsupervised-classification method that does not establish any training sites. The results showed that the urban area of the object increased by 145% due to housing-site development. The image data from 2009 had a classification accuracy of 98%. For method verification, the results were compared to land-cover changes through Post-classification comparison. The maximum utilization of the available data within multiple images and the optimized classification allowed for an improvement in the classification accuracy. The proposed rule-based image-classification method is expected to be widely employed for the time-series analysis of images to produce a thematic map for urban development and to monitor urban development and environmental change.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.139-148
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• 2010

This paper shows a reinforcement post-processing method and feedback algorithm for improvement of assigning method in classification. Especially, we focused on complex documents that are generally considered to be hard to classify. A basis factors in traditional classification system are training methodology, classification models and features of documents. The classification problem of the documents containing shared features and multiple meanings, should be deeply mined or analyzed than general formatted data. To address the problems of these document, we proposed a method to expand classification scheme using decision boundary detected automatically in our previous studies. The assigning method that a document simply decides to the top ranked category, is a main factor that we focus on. In this paper, we propose a post-processing method and feedback algorithm to analyze the relevance of ranked list. In experiments, we applied our post-processing method and one time feedback algorithm to complex documents. The experimental results show that our system does not need to change the classification algorithm itself to improve the accuracy and flexibility.