• STIMA Bulletin
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• s.2
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• pp.27-45
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• 2005

• Journal of Korean Library and Information Science Society
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• v.39 no.3
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• pp.369-386
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• 2008

The purpose of this study is investigate to compare with knowledge classification and library classification system of botany. First, the knowledge field of botany is mainly classified in morphology, physiology, ecology, taxonomy, genetics, evolution and others by the study object of plants. Second, the division of plants is treated in the field of taxonomy, that is, a lower subdivision study of botany, and Engler's classification is still prevalent in the taxonomy. Third, in library classification, KDC, NDC, UDC and CC adopted the Engler's classification, but DDC and LCC was taken of the Bentham & Hooker's classification. In the Engler's classification, plants are arranged by evolution's order, from lower vegetation to higher vegetation, but Bentham & Hooker's classification is arranged in the reverse order. Forth, it is desirable that every plants(482-489) of KDC' botany are subdivided by the attribute or structure of plants being treated in the general botany as if they are subdivided in the DDC or CC.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.39-57
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• 2001

Categorical data has sometimes misclassification errors. If this data will be analyzed, then estimated cell probabilities could be biased and the standard Pearson X2 tests may have inflated true type I error rates. On the other hand, if we regard wellclassified data with misclassified one, then we might spend lots of cost and time on adjustment of misclassification. It is a necessary and important step to ask whether categorical data is misclassified before analyzing data. In this paper, when data is misclassified at one of two variables for two-dimensional contingency table and marginal sums of a well-classified variable are fixed. We explore to partition marginal sums into each cells via the concepts of Bound and Collapse of Sebastiani and Ramoni (1997). The double sampling scheme (Tenenbein 1970) is used to obtain informations of misclassification. We propose test statistics in order to solve misclassification problems and examine behaviors of the statistics by simulation studies.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1204-1217
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• 2004

Numerous statistical and machine learning techniques have been studied for automatic text classification. However, because they train the classifiers using only feature vectors of documents, ambiguity between two possible categories significantly degrades precision of classification. To remedy the drawback, we propose a new method which incorporates relationship information of categories into extant classifiers. In this paper, we first perform the document classification using the k-NN classifier which is generally known for relatively good performance in spite of its simplicity. We employ the relationship information from an object-based thesaurus to reduce the ambiguity. By referencing various relationships in the thesaurus corresponding to the structured categories, the precision of k-NN classification is drastically improved, removing the ambiguity. Experiment result shows that this method achieves the precision up to 13.86% over the k-NN classification, preserving its recall.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.207-218
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• 2014

The information on the land cover along stream corridor is important for stream restoration and maintenance activities. This study aims to review the different classification methods for mapping the status of stream corridors in Seom River using airborne RGB and CIR digital ortho imagery with a ground pixel resolution of 0.2m. The maximum likelihood classification, minimum distance classification, parallelepiped classification, mahalanobis distance classification algorithms were performed with regard to the improvement methods, the skewed data for training classifiers and filtering technique. From these results follows that, in aerial image classification, Maximum likelihood classification gave results the highest classification accuracy and the CIR image showed comparatively high precision.

• Korean Journal of Plant Resources
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• v.29 no.2
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• pp.171-188
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• 2016

This study was carried out to investigate the flora of Mt. Baegam (1,099 m) from March, 2014 to July, 2015. The vascular plants were summarized as 506 taxa, including 90 families, 293 genera, 435 species, 5 subspecies, 58 varieties and 8 forms. Among the investigated 506 taxa, 14 Korean endemic, 16 rare plants and 80 specially designated plants (V degree: 3 taxa, IV degree: 7 taxa, III degree: 22 taxa, II degree: 21 taxa, I degree: 27 taxa) by the Ministry of Environment were also included. The naturalized plants were 36 taxa including 3 ecosystem disturbance wild plant. The urbanization index and percent of naturalized plants species were estimated as 11.2% and 7.1%, respectively. Useful plants of 506 taxa listed consists of 207 taxa (40.9%) of edible plants, 200 taxa (39.5%) of pasture plants, 146 taxa (28.9%) of medicinal plants, 58 taxa (11.5%) of ornamental plants and 15 taxa (3.0%) of timber plants, respectively.

• Journal of the Korean Society for Nondestructive Testing
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• v.17 no.3
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• pp.162-173
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• 1997

Automatic on-line surface inspection systems have been applied for monitoring a quality of steel strip surfaces. One of the important issues in this application is the performance of on-line defect classifiers. Rule-based classification table methods which are conventionally used for this purpose have been suffered from their low performances. In this work, probabilistic neural networks and the enhanced classification tables which are newly proposed here are applied as alternative on-line classifiers to identify types of surface defects on cold rolled strips. Probabilistic neural networks have shown very excellent performance for classification of surface defects.

• Journal of Information Management
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• v.43 no.3
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• pp.79-95
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• 2012

This study is designed for library classification of the tourism. Contents studies are summarized as follows. First, an analysis of the current status of classification and items by a key work 'tourism' in KDC, NDC, DDC, UDC, and LCC which are main library classification tables today indicates that they are too limited to travelling and tourism, tourism policy, and tourist attractions etc. Second, we divided areas studies newly based on the theoretical books in order to extract the classification items of the tourism field, and comparatively analyzed main library classification systems today. We divided basic categories of the classification items into four including tourism general, tourists, tourism attraction, tourism media. Third, a new classification was designed for library classification of the tourism field. Basically, the tourism field was assigned to the main four items and the classification items which were weighted or scattered were adjusted.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2018.04a
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• pp.46-46
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• 2018

본 연구는 봉화군 청옥산 산림유전자원보호구역에 분포하는 관속식물을 파악하고자 수행하였다. 청옥산 보호구역은 산림청에서 '희귀식물자생지' 유형으로 지정하여 관리하고 있으며, 면적은 약 2,051ha에 달하며 주로 신갈나무군락과 소나무군락이 우점하고있다. 현지조사는 2017년 3월부터 10월까지 계절별로 수행하였다. 조사결과, 보호구역내 관속식물은 86과 258속 390종 4아종 54변종 6품종으로 총 454분류군의 분포가 확인되었다. 이 중 한국특산식물은 가야물봉선, 처녀치마, 할미밀망, 키버들 등 11분류군이며, 산림청 지정 희귀식물은 취약종(VU)에 주목, 백작약, 꼬리진달래 3분류군, 약관심종(LC)에 등칡, 도깨비부채, 정향나무, 말나리, 나도개감채 등 14분류군이다. 식물구계학적 특정식물로는 IV등급인 회리바람꽃, 가지괭이눈, 산겨릅나무, 귀박쥐나물 등 10분류군, III등급인 애기감둥사초, 중나리, 토현삼, 선갈퀴, 금강제비꽃, 노랑갈퀴, 분비나무 등 23분류군, II등급 32분류군, I등급 30분류군이 확인되었다. 외래식물은 오리새, 털별꽃아재비, 왕포아풀, 주걱개망초, 지느러미엉겅퀴, 족제비싸리 등 18분류군이 임도 주변에서 확인되었다. 조사된 관속식물을 용도별로 구분하면 식용식물 181분류군, 섬유용식물 6분류군, 약용식물 143분류군, 관상용식물 52분류군, 사료용식물 172분류군, 염료용식물 9분류군, 목재용식물 22분류군으로 분석되었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.517-525
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• 1999

In this paper, we propose efficient total recognition system of handwritten and printed numerals for reducing the classification time. The proposed system consists of two-step neuroclassifier : Printed numerals classifier and handwritten numerals classifier. In the proposed scheme, the printed numerals classifier classifies the printed numerals rapidly with single MLP neural network by low-order feature vector and rejects handwritten numerals. The handwritten numerals classifier classifies the handwritten numerals which is rejected in printed numerals classifier with modularized cluster neural network by complex feature vector. In order to verify the performance of the proposed method,handwritten numerals database of NIST and printed numerals database which include various fonts are used in the experiments. In case of using the proposed classifier, the overall classification time was reduced by 49.1% - 65.5% in comparison of the existent handwritten classifier.