• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.69-90
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• 2022

As basic data that can systematically support and evaluate R&D activities as well as set current and future research directions by grasping specific trends in domestic academic research, I sought efficient ways to assign standardized subject categories (control keywords) to individual journal papers. To this end, I conducted various experiments on major factors affecting the performance of automatic classification, focusing on feature selection techniques, for the purpose of automatically allocating the classification categories on the National Research Foundation of Korea's Academic Research Classification Scheme to domestic journal papers. As a result, the automatic classification of domestic journal papers, which are imbalanced datasets of the real environment, showed that a fairly good level of performance can be expected using more simple classifiers, feature selection techniques, and relatively small training sets.

• Communications of Mathematical Education
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• v.23 no.3
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• pp.683-705
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• 2009

In this study we classify and analyze 265 papers which had been published in the Journal Series A in Korean Society of Mathematics Education from year 2000 to year 2008. We have also studied all the papers in the Journal Series A in Korean Society of Mathematics Education last 46 years based on Professor Lee, Gang-sup's paper 'A Classification and Analysis of the Articles in -From issue 1 to issue 99-'.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.48-51
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• 2018

컴퓨터 및 기술의 발전으로 힘입어 수많은 논문이 오프라인뿐 아니라 온라인으로 발행되고 있고, 새로운 분야들도 계속 생기면서 사용자들은 방대한 논문들 중 자신이 필요로 하는 논문을 검색하거나 분류하기에 많은 어려움을 겪고 있다. 이러한 한계를 극복하기 위해 본 논문에서는 유사 내용의 논문을 분류하고 이를 군집화하는 방법을 제안한다. 제안하는 방법은 TF-IDF를 이용하여 각 논문의 초록으로 부터 대표 주제어를 추출하고, K-means 클러스터링 알고리즘을 이용하여 추출한 TF-IDF 값을 근거로 논문들을 유사 내용의 논문으로 군집화한다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.1
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• pp.25-44
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• 2013

The purpose of this study was to analyze the trends of Classification Studies in Korea from 205 research articles in five scholarly journals in Library and Information Science during the period 1986-2011. Individual article was analyzed in the aspects of specific research topics, research methodologies, data collection & analysis, and the characteristics of classification research was suggested. The amount of research paper has increased during the period and the focus of the study was on practical use and most of the papers were about new classification schedules or modification of current classification systems. Most of the papers were literature research and comparative research on classification systems by professors or graduate students. Top 9 authors in classification research were professors or lecturers and a few authors were computer science major. Therefore, various research topics, research methodologies and collaborations with other disciplines are necessary for future classification research.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.4
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• pp.451-455
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• 2008

In this paper, the k-nearest templates method is proposed to combine multiple classifiers effectively. First, the method decomposes training samples of each class into several subclasses based on the outputs of classifiers to represent a class as multiple models, and estimates a localized template by averaging the outputs for each subclass. The distances between a test sample and templates are then calculated. Lastly, the test sample is assigned to the class that is most frequently represented among the k most similar templates. In this paper, C-means clustering algorithm is used as the decomposition method, and k is automatically chosen according to the intra-class compactness and inter-class separation of a given data set. Since the proposed method uses multiple models per class and refers to k models rather than matches with the most similar one, it could obtain stable and high accuracy. In this paper, experiments on UCI and ELENA database showed that the proposed method performed better than conventional fusion methods.

• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.153-162
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• 2007

Sasang medicine is peculiar medicine that constitution of a human classify four types and differ treatment method by physical constitution. In this way the most important thing is very difficult problem that classification of Sasang constitution and discriminate correctly. Therefore, in this paper targets diagnosis medical appliances development of hybrid form that can behave constitution classification and sees among for this paper to propose about method to grasp characteristic that is morphology about eye, nose, ear and mouth be based on appearance and manner of speaking. In this paper, classified and verified this for Sasang constitution through the QSCC II program at 1 step and present method that more exactly and conveniently analyzing measure each physical constitution feature by survey about eye, nose, ear and mouth at 2 steps. Also, extraction and analyze and verified main area of physical constitution classification based on front face and side face at 3 steps. Such propose method to extraction the principal face region based on face color from front face and side face for correct physical constitution classification diagnosis appliance development through experiment consideration and verification process.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.405-413
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• 2012

Low misclassification performance is significant with high classification accuracy for a reliable diagnosis of ECG signals, and diagnosing abnormal state as normal state can especially raises a deadly problem to a person in ECG test. In this paper, we propose detection and classification method of abnormal rhythm by rule-based rhythm classification reflecting clinical criteria for disease. Rule-based classification classifies rhythm types using rule-base for feature of rhythm section, and rule-base deduces decision results corresponding to professional materials of clinical and internal fields. Experimental results for the MIT-BIH arrhythmia database show that the applicability of proposed method is confirmed to classify rhythm types for normal sinus, paced, and various abnormal rhythms, especially without misclassification in detection aspect of abnormal rhythm.

• Journal of KIISE:Software and Applications
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• v.35 no.1
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• pp.41-49
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• 2008

Fingerprint classification is useful to reduce the matching time of a huge fingerprint identification system by categorizing fingerprints into predefined classes according to their global features. Although global features are distributed diversly because of the uniqueness of a fingerprint, previous fingerprint classification methods extract global features non-adaptively from the fixed region for every fingerprint. We propose an novel method that extracts features adaptively for each fingerprint in order to classify various fingerprints effectively. It extracts ridge directional values as feature vectors from the region after searching the feature region by calculating variations of ridge directions, and classifies them using support vector machines. Experimental results with NIST4 database show that we have achieved a classification accuracy of 90.3% for the five-class problem and 93.7% for the four-class problem, and proved the validity of the proposed adaptive method by comparison with non-adaptively extracted feature vectors.

• The KIPS Transactions:PartC
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• v.18C no.4
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• pp.243-250
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• 2011

Nowadays, the traffic type and behavior are extremely diverse due to the appearance of various services on Internet, which makes the need of traffic identification important for efficient operation and management of network. In recent years traffic identification methodology using statistical features of flow has been broadly studied. We also proposed a traffic identification methodology using payload size distribution in our previous work, which has a problem of low completeness. In this paper, we improved the completeness by solving the PSD conflict using IP and port. And we improved the accuracy by changing the distance measurement between flow and statistic signature from vector distance to per-packet distance. The feasibility of our methodology was proved via experimental evaluation on our campus network.

• The KIPS Transactions:PartB
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• v.19B no.4
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• pp.237-242
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• 2012

Web information service needs a document classification system for efficient management and conveniently searches. Existing document classification systems have a problem of low accuracy in classification, if a few number of feature words is selected in documents or if the number of documents that belong to a specific category is excessively large. To solve this problem, we propose a document classification system using 'Modified ECCD' feature selection method and 'Category Weight for each Document'. Experimental results show that the 'Modified ECCD' feature selection method has higher accuracy in classification than ${\chi}^2$ and the ECCD method. Moreover, combining the 'Category Weight for each Document' feature value and 'Modified ECCD' feature selection method results better accuracy in classification.