• 응용통계연구
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• 제23권6호
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• pp.1093-1102
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• 2010

비모수적 판별분류방법인 k-Nearest Neighbors Classification(KNNC) 방법은 널리 사용되고 있지만 고정된 이웃의 개수를 사용하며 또한 집단변수의 정보를 활용하지 않음으로서 자료의 국소적 특징을 반영하지 못하는 단점이 있다. Adaptive Nearest Neighbors Classification(ANNC) 방법과 Modified k-Nearest Neighbors Classification(MKNNC) 방법은 각각 이러한 단점들을 보완하기 위해 제안된 방법이다. 본 연구에서는 ANNC 방법과 MKNNC 방법의 장점을 결합한 Modified Adaptive Nearest Neighbors Classification(MANNC) 방법을 제안하였다. 나아가, 제안된 방법의 활용 가능성을 살펴보고자 실제자료에 대한 분석과 모의실험을 통해 기존의 방법들과 비교하였다.

• 한국도서관정보학회지
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• 제23권
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• pp.439-469
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• 1995

The purpose of this study is to develop a new domestic (national) law classification scheme with universality. An underlying reason for the development of this scheme reset upon the fact that Civil law system, Common law system, Socialistic law system have had difficulties each other and that current classification scheme covering three law systems have not been still in existence. From the comparative discussion of classification schemes that are the representative of each law system, a new national law classification scheme with universality was designed. If law classification scheme have been completeness, this new scheme must be combined with jurisprudence and international law classification scheme which was developed already.

• International Journal of Railway
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• 제2권1호
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• pp.37-42
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• 2009

Information management of products and services in every industries is gaining importance for resource planning and maintenance. In this paper, we analyzed the information classification systems for railway industry. International and domestic classification systems, such as HS, UNSPSC, eCl@ss and ISIC, are reviewed; as a result this paper presents the findings and the various issues. We proposed to-be images in adopting and utilizing the classification systems. Using the integrative information classification systems could make efficient electronic procurement, supply chain management and e-Business of railway services.

• Communications for Statistical Applications and Methods
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• 제28권2호
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• pp.135-150
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• 2021

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

• 한국정보과학회논문지:데이타베이스
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• 제34권1호
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• pp.35-45
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• 2007

이 논문에서는 교육용 저장소 자원의 체계적인 관리를 위한 분류 모델을 제안한다. 생성되는 자원들에 대한 체계적인 저장 및 관리, 정확한 검색, 그리고 활용성을 극대화하기 위해서는 정확한 분류 체계가 요구된다. 그러나 교육용 저장소 자원을 위한 분류 체계나 분류 모델에 대한 연구는 미비한 수준이다. 이는 교육 자원의 비효율적 관리, 부정확한 검색 및 낮은 활용성 등의 문제점을 초래한다. 상품 정보와 관련된 분야에서는 다양한 분류 체계에 대한 연구가 이루어져 왔다. 그러나 교육 자원 정보와 기존연구 분야의 정보는 서로 다른 특성을 지닌다. 따라서 교육용 저장소 내 자원 관리를 위한 분류 체계 및 분류 모델에 대한 연구가 요구된다. 교육 자원들에 대한 효율적이고 편리한 활용을 위해서는 여러 관점을 반영하는 분류 체계에 따라 자원들을 일관성 있게 유지 관리하여야 한다. 이 논문에서는 교육 자원의 체계적인 관리 및 활용성 향상을 위한 분류 모델을 제안한다. 즉, 교육용 저장소의 자원들에 대한 분류 체계를 다양한 관점에 따라 동적으로 유지할 수 있는 분류 모델을 제안한다. 이러한 목적을 위해 먼저 관련된 과학기술분야 분류 체계들을 바탕으로 구현 자원들에 적합한 분류 체계를 정의한다. 특히 정의된 분류 체계를 동적으로 유지 관리할 수 있는 분류 모델을 정의한다. 제안된 분류 체계 및 분류 모델은 보다 정확하고 체계적인 구현 자원에 대한 관리를 가능하게 하며 또한 활용의 용이성을 향상시킨다.

• 한국측량학회지
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• 제35권4호
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• pp.303-312
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• 2017

This paper indirectly created high density point cloud data using unmanned aerial vehicle image. Then, we tried to suggest new concept of classification technique where particular objects from point cloud data can be selectively classified. For this, we established the classification technique that can be used as search factor in classifying color information in point cloud data. Then, using suggested classification technique, we implemented object classification and analyzed classification accuracy by relative comparison with self-created proof resource. As a result, the possibility of point cloud data classification was observable using the image's information. Furthermore, it was possible to classify particular object's point cloud data in high classification accuracy.

• 산업공학
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• 제18권1호
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• pp.10-21
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• 2005

With the increasing proliferation of World Wide Web, electronic mail systems have become very widely used communication tools. Researches on e-mail classification have been very important in that e-mail classification system is a major engine for e-mail response management systems which mine unstructured e-mail messages and automatically categorize them. In this research we compare the performance of Naive Bayesian learning and Centroid-Based Classification using the different data set of an on-line shopping mall and a credit card company. We analyze which method performs better under which conditions. We compared classification accuracy of them which depends on structure and size of train set and increasing numbers of class. The experimental results indicate that Naive Bayesian learning performs better, while Centroid-Based Classification is more robust in terms of classification accuracy.

• 한국도서관정보학회지
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• 제33권1호
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• pp.143-167
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• 2002

이 연구는 외국의 문헌분류법이 중국의 문헌분류법에 끼친 영향을 DDC, ББK를 중심으로 파악하였다. 아울러 도서관을 국책 수행 상 중요 기관으로 인식하는 중국의 도서관에서 대표적으로 사용하고 있는 중국의 현대 3대 문헌분류법인 중국인민대학도서관도서분류법, 중국과학원도서관도서분류법, 중국 도서관분류법에 대하여 발전과정을 중심으로 분석하였다.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2004년도 추계학술발표회 논문집
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• pp.251-256
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• 2004

The image classification is one of the most important studies in the remote sensing. In general, the MLC(Maximum Likelihood Classification) classification that in consideration of distribution of training information is the most effective way but it produces a bad result when we apply it to actual hyperspectral image with the same classification technique. The purpose of this research is to reveal that which one is the most effective and suitable way of the classification algorithms iii the hyperspectral image classification. To confirm this matter, we apply the MLC classification algorithm which has distribution information and SAM(Spectral Angle Mapper), SFF(Spectral Feature Fitting) algorithm which use average information of the training class to both multispectral image and hyperspectral image. I conclude this result through quantitative and visual analysis using confusion matrix could confirm that SAM and SFF algorithm using of spectral pattern in vector domain is more effective way in the hyperspectral image classification than MLC which considered distribution.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.204-206
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• 2003

In order to utilize remote sensed images effectively, a lot of image classification methods are suggested for many years. But, the accuracy of traditional methods based on pixel-based classification is not high in general. In this study, object oriented classification based on image segmentation is used to classify Landsat images. A necessary prerequisite for object oriented image classification is successful image segmentation. Object oriented image classification, which is based on fuzzy logic, allows the integration of a broad spectrum of different object features, such as spectral values , shape and texture. Landsat images are divided into urban, agriculture, forest, grassland, wetland, barren and water in sochon-gun, Chungcheongnam-do using object oriented classification algorithms in this paper. Preliminary results will help to perform an automatic image classification in the future.