• Journal of Korean Library and Information Science Society
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• v.41 no.4
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• pp.151-170
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• 2010

There is a growing interest in user-centered classification or reader-interest classification, as questions have arisen from the meanings and the effects of traditional library classification. American public libraries have used fiction genre classification called bookstore model as an alternative to the traditional classification schemes. As a result, accessibility to the collection was promoted and library service for their users was improved. This study intends to make a comprehensive inquiry about the philosophical background and functional features of genre classification. To the end, literature survey and interviews or e-mails with librarians in American public libraries were conducted.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.2 s.16
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• pp.47-60
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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%.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04d
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• pp.521-523
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• 2003

본 논문은 웹 탐색 중 디렉토리 검색엔진의 분류검색에 대한 문제점을 해결하기 위해서 분류와 검색어간의 관계를 퍼지논리를 이용하여 계산하고 분류간의 함의관계를 유도함으로써 동적인 분류체계를 구성하는 새로운 방법을 제시한다. 이 방법의 장점은 분류간의 함의관계를 유사한 하위분류로서 간주함으로써 분류검색 결과의 재현율을 높일 수 있다는 것이다.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2018.04a
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• pp.28-28
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• 2018

본 연구는 강원도 인제군 매봉 지역 일대를 중심으로 식물상 조사를 수행하였다. 관속식물은 73과 173속 228종 3아종 28변종 6품종으로 총 265분류군으로 조사되었다. 분류군별로는 양치식물이 5과 14분류군, 나자식물 1과 3분류군, 쌍자엽식물 60과 182분류군, 단자엽식물 7과 29분류군으로 확인되었다. 주요 식물로는 특산식물 고려엉겅퀴(Cirsium setidens), 무늬족도리풀(Asarum versicolor), 병꽃나무(Weigela subsessilis) 등 8과 9분류군, 희귀식물은 멸종위기종(CR) 긴개별꽃(Pseudostellaria japonica), 애기가물고사리(Woodsia glabella) 2분류군, 위기종(EN) 댕강나무(Abelia mosanensis) 1분류군, 취약종(VU) 멱쇠채(Scorzonera austriaca subsp. glabra) 1분류군, 약관심종(LC)인 과남풀(Gentiana triflora var. japonica), 금강제비꽃(Viola diamantiaca), 너도바람꽃(Eranthis stellata) 등 9분류군으로 총 13분류군이 확인되었다. 환경부지정 특정식물종은 V등급 1분류군, IV등급 2분류군, III등급 13분류군이 확인되었다. 귀화식물은 단풍잎돼지풀(Ambrosia trifida), 달맞이꽃(Oenothera biennis), 닭의덩굴(Fallopia dumetorum) 등 6과 11분류군이 확인되었다. 산림청에서 지정한 특별산림보호대상종은 댕강나무(Abelia mosanensis), 참배암차즈기(Salvia chanryoenica)가 확인되었다. 유용식물로는 용도를 모르는 96분류군(36.2%)를 제외하면 169분류군(63.8%)이 자원식물로 이용가치가 있는 것으로 나타났다. 이들을 용도에 따라 구분해보면 식용 114분류군(33.2%), 섬유용 5분류군(1.5%), 약용 82분류군(23.9%), 관상용 33분류군(9.6%), 사료용 94분류군(27.4%), 산업용 1분류군(0.3%), 염료용 3분류군(0.9%), 목재용 11분류군(3.2%)으로 확인되었다.

• Korean Journal of Environment and Ecology
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• v.32 no.1
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• pp.1-22
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• 2018

The purpose of this study is to provide the baseline data for conservation and management of Korean forest ecosystem by surveying and analyzing the vascular plants distributed from Gitdaebaegibong to Cheonghwasan in Baekdudaegan Mountains. The results are as follows. The numbers of vascular plants in the whole survey section were summarized as 771 taxa including 103 families, 379 genera, 623 species, 4 subspecies, 121 varieties and 23 forms. There were 377 taxa in A-section, 395 taxa in B-section, 278 taxa in C-section, 325 taxa in D-section, 534 taxa in E-section, and 406 taxa in F-section. The rare plants were 32 taxa including Megaleranthis saniculifolia, Rodgersia podophylla, Iris ensata var. spontanea, and Gastrodia elata. In IUCN Red List categories, there were 1 taxon of CR, EN, and DD each, 11 taxa of the VU, and 18 taxa of the LC. The Korean endemic plants were 26 taxa including Asarum versicolor, Clematis fusca var. coreana, Vicia chosenensis, Stewartia pseudocamellia, Carex okamotoi, and Luzula sudetica var. nipponica. The specific plants by floristic region were 143 taxa including 3 taxa of grade V, 12 taxa of grade IV, 41 taxa of grade III, 42 taxa of grade II, and 45 taxa of grade I. The naturalized plants were 41 taxa including Rumex crispus, Ailanthus altissima, Erechtites hieracifolia, Erigeron annuus, and Poa pratensis. The invasive alien plants were 4 taxa including Rumex acetocella, Sicyos angulatus, Ambrosia artemisiifolia, and Aster pilosus. The plants adaptable to climate change were 43 taxa including 14 taxa of endemic plants, 2 taxa of southern plants, and 27 taxa of northern plants.

• Journal of Korean Library and Information Science Society
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• v.48 no.3
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• pp.45-61
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• 2017

The objective of this study is to understand the application of relative location system and minute classification system in the DDC and to identify the effect of the relative location system and minute classification system during the late of 19th century. In order to achieve the objective, four main investigation areas were chosen: relative location system, minute classification system, and DDC influence to other libraries and classification systems. First, DDC applied a relative location system revolutionarily instead of a fixed location system for arranging books on the shelves, so it opened the period of modern library classification systems. Second, it used a minute classification system, and could classify books which had minute subjects. Third, it applied form to a criterion for dividing divisions and sections, so it helped for classifying books. Fourth, it used a numerical decimal system as a classification system, then people could use it economically and practically. Last, DDC influenced modern classification system such as the Expansive Classification and the Subject Classification etc. DDC is a suitable library classification system for the needs of the times, and it is a practical classification system for each library.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.378-385
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• 2010

The explosive increase in the use of email has made to need email classification efficiently and accurately. Current work on the email classification method have mainly been focused on a binary classification that filters out spam-mails. This methods are based on Support Vector Machines, Bayesian classifiers, rule-based classifiers. Such supervised methods, in the sense that the user is required to manually describe the rules and keyword list that is used to recognize the relevant email. Other unsupervised method using clustering techniques for the multi-category classification is created a category labels from a set of incoming messages. In this paper, we propose a new automatic email multi-category classification method using NMF for automatic category label construction method and dynamic category hierarchy method for the reorganization of email messages in the category labels. The proposed method in this paper, a large number of emails are managed efficiently by classifying multi-category email automatically, email messages in their category are reorganized for enhancing accuracy whenever users want to classify all their email messages.

• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1124-1134
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• 2003

The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. It is essential to efficiently analyze DNA microarray data because the amount of DNA microarray data is usually very large. Since accurate classification of cancer is very important issue for treatment of cancer, it is desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. Generally combining classifiers gives high performance and high confidence. In spite of many advantages of ensemble classifiers, ensemble with mutually error-correlated classifiers has a limit in the performance. In this paper, we propose the ensemble of neural network classifiers learned from negatively correlated features using three benchmark datasets to precisely classify cancer, and systematically evaluate the performances of the proposed method. Experimental results show that the ensemble classifier with negatively correlated features produces the best recognition rate on the three benchmark datasets.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.35-40
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• 2015

The paper proposes a meta-learning approach for building multi-classifier systems in a GA-based inductive learning environment. In our meta-learning approach, a classifier consists of a general classifier and a meta-classifier. We obtain a meta-classifier from classification results of its general classifier by applying a learning algorithm to them. The role of the meta-classifier is to evaluate the classification result of its general classifier and decide whether to participate into a final decision-making process or not. The classification system draws a decision by combining classification results that are evaluated as correct ones by meta-classifiers. We present empirical results that evaluate the effect of our meta-learning approach on the performance of multi-classifier systems.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.267-270
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• 2007

기계학습에서 분류기들의 집합으로 구성된 앙상블 분류기는 단일 분류기에 비해 정확도가 높다는 것이 입증되었다. 본 논문에서는 새로운 앙상블 학습으로서 데이터의 지역 기반 분류기들의 앙상블 학습을 제시하여 기존의 앙상블 학습과의 비교를 통해 성능을 검증하고자 한다. 지역 기반 분류기의 앙상블 학습은 데이터의 분포가 지역에 따라 다르다는 점에 착안하여 학습 데이터를 분할하고 해당하는 지역에 기반을 둔 분류기들을 만들어 나간다. 이렇게 만들어진 분류기들로부터 지역에 따라 가중치를 둔 투표를 하여 앙상블 방법을 이끌어낸다. 본 논문에서 제시한 앙상블 분류기의 성능평가를 위해 UCI Machine Learning Repository에 있는 11개의 데이터 셋을 이용하여 단일 분류기와 기존의 앙상블 분류기인 배깅과 부스팅등의 정확도를 비교하였다. 그 결과 기본 분류기로 나이브 베이즈와 SVM을 사용했을 때 새로운 앙상블 방법이 다른 방법보다 좋은 성능을 보이는 것을 알 수 있었다.