• 한국기록관리학회지
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• 제3권2호
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• pp.1-33
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• 2003

이 논문은 문서분류에 대한 이론적 근거를 찾아보고, 조선조 이후 구한말을 걸쳐 대한민국 정부수립 이후 지금까지 시행해오고 있는 한국공문서분류의 변천과정을 살펴, 새로 제정 공포될 '기록물분류기준표'와 비교하여, 시행상의 문제점을 검토함으로써 더 나은 분류표로 발전시키는데 목적을 둔다.

• 한국농공학회논문집
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• 제49권1호
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• pp.23-33
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• 2007

Classification results of rural villages provide useful information about rural village characteristics to select similar villages in rural development project; many researches about regional classification have been practiced. Recently rural amenity was introduced as an alternative for rural development, and rural villages have been surveyed to find potential resources for rural development by 'Rural Amenity Resources Survey Project'. Accumulated information through this survey project could be used to classify rural villages. However existing rural classification method using statistical data is not efficient method to use rural amenity resources information described with text. We introduced Information Bottleneck Method (IBM) based on information theory and implemented this method to classification with rural amenity resources information of Yanggang-myen, Yeongdong-gun in Chungbuk province.

• Spatial Information Research
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• 제12권1호
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• pp.111-125
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• 2004

위성영상 분류에 관한 주요 주제 중 하나는 분류 정확도 향상에 있다. 동일지역에 대한 동일시기의 위성영상을 취득할 수 있는 기회가 많아지는 현실을 감안할 때, 복수의 위성영상 데이터를 이용하여 분류정확도가 향상된 분류결과를 도출하는 것은 의미 있는 일일 것이다. 본 연구 주제는 최대우도법을 사용하여 계산된 데이터의 사후확률 및 분류 불확실도를 Dempster-Shafer의 증거이론에 적용하여 분류정확도를 향상시키고자 하는 것이다. 분석결과 개별적인 데이터 분류나 데이터간 융합에 의한 분류보다 본 연구에서 제안한 방법이 전체정확도와 Kappa 지수 모두 높은 정확도를 나타냈으며, 정확도 차에 대한 검정을 실시하여 본 연구에서 제안한 방법이 다른 방법에 비해 우수함을 통계적으로 증명하였다.

• Journal of Information Processing Systems
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• 제11권3호
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• pp.421-437
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• 2015

This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on information fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the performance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.

• 정보관리학회지
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• 제20권1호
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• pp.165-198
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• 2003

본 연구는 문헌정보학 분야 연구논문에서 저자들의 이론 활용 정도를 분석하기 위하여 한국문헌정보학회지와 정보관리학회지를 대상으로 지난 30년간 연구된 654편의 논문에 대한 내용분석이 이루어 졌다. 주요 연구내용은 연구논문의 연대별 생산성, 세부주제영역별 생산성, 연구에 활용된 이론의 유형과 근원, 개별 이론별 활용도, 세부주제 영역별 활용도, 학회지별 활용도 등에 대한 개념적 연구와 실증적 연구가 수행되었다. 이를 위하여 이론의 개념적 기준 설정과 문헌정보연구의 세부주제영역에 대한 새로운 분류 체계, 특히 이론의 활용성을 평가하기 위한 기준으로 ‘이론 활용 5단계’ 모델을 제시하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.281-285
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• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.

• 한국문헌정보학회지
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• 제50권2호
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• pp.101-118
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• 2016

문헌분류체계에서 복합주제의 전개를 위해서는 복수의 구분기준 혹은 특성(characteristics)을 적용해야 한다. 복합주제의 세분전개를 위해 채택된 복수의 특성들은 세분의 매 단계에서 하나씩 순차적으로 전개된다. 복합주제를 세분 전개하는 과정에서 복수의 구분기준을 적용하는 순서를 인용순(citation order)이라 한다. 주제를 세분하는 과정에 적용되는 구분기준의 전개순서인 인용순이 구체적이고 일관성을 가져야 문헌분류체계의 일관성과 논리성을 확보할 수 있다. 특히 열거식 분류체계의 경우에는 복합주제의 표현을 위한 인용순의 결정이 분류체계의 구성과 체계성에 큰 영향을 미친다. 인용순 및 그 적용과 관련된 분류이론상의 기본 원칙들은 실제 분류체계 구축과정에서 실용적인 이유로 완벽히 적용되기 어렵다. 이에 본고는 분류체계의 구축과정에서 적용되고 있는 인용순 관련 분류이론과 선행연구를 개괄해보고, 인용순 실제 적용상의 제반 사항을 고찰하였다.

• 대한원격탐사학회지
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• 제22권1호
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• pp.75-85
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• 2006

This paper has dealt with a data fusion for the problem of land-cover classification using multisensor imagery. Dempster-Shafer evidence theory has been employed to combine the information extracted from the multiple data of same site. The Dempster-Shafer's approach has two important advantages for remote sensing application: one is that it enables to consider a compound class which consists of several land-cover types and the other is that the incompleteness of each sensor data due to cloud-cover can be modeled for the fusion process. The image classification based on the Dempster-Shafer theory usually assumes that each sensor is represented by a single channel. The evidential approach to image classification, which utilizes a mass function obtained under the assumption of class-independent beta distribution, has been discussed for the multiple sets of mutichannel data acquired from different sensors. The proposed method has applied to the KOMPSAT-1 EOC panchromatic imagery and LANDSAT ETM+ data, which were acquired over Yongin/Nuengpyung area of Korean peninsula. The experiment has shown that it is greatly effective on the applications in which it is hard to find homogeneous regions represented by a single land-cover type in training process.

• 아동학회지
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• 제20권3호
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• pp.259-273
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• 1999

This studied investigated children's music identification, classification, and seriation cognitive task performance abilities by age and sex. The subjects were l20 six-, eight-, and ten-year-old school children. There were significant positive correlations among music cognition tasks and significant age and sex differences within each of the music tasks. Ten-year-old children were more likely to complete their music identification tasks than the younger children and girls were more likely than boys to complete their music identification tasks. Eight- and 10-year-old children were more likely to complete their music classification tasks than the younger group. Piagetian stage theory was demonstrated in children's music classification task performance. There was an age-related increase in the performance of the music seriation tasks. Developmental sequential theory was demonstrated in music seriation performance.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.170-174
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• 1997

This paper studies a method for making more efficient classification rules in the ID3 using the rough set theory. Decision tree technique of the ID3 always uses all the attributes in a table of examples for making a new decision tree, but rough set technique can in advance eleminate dispensable attributes. And the former generates only one type of classification rules, but the latter generates all the possibles types of them. The rules generated by the rough set technique are the simplist from as proved by the rough set theory. Therefore, ID3, applying the rough set technique, can reduct the size of the table of examples, generate the simplist form of the classification rules, and also implement an effectie classification system.