• Journal of Information Management
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• v.39 no.4
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• pp.47-66
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• 2008

This study introduced the machine learning algorithms to overcome the many different limitations involved with manual classification and to provide the users with faster and more accurate classification service. The experiments objects of the study were consisted of 100 literature titles for each of the eight subject categories in MeSH. The algorithms used to the experiments included Neural network, C5.0, CHAID and KNN. As results, the combination of the neural network and C5.0 technique recorded classification accuracy of 83.75%, which was 2.5% and 3.75% higher than that of the neural network alone and C5.0 alone, respectively. The number represented the highest accuracy rates among the four classification experiments. Thus the use of the neural network and C5.0 technique together will result in higher accuracy rates than the techniques individually.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.47-60
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• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

• Journal of the Korean Society for information Management
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• v.40 no.1
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• pp.1-21
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• 2023

This study specifically reviewed the performance of the ranking schemes as an efficient feature selection method for text classification. Until now, feature ranking schemes are mostly based on document frequency, and relatively few cases have used the term frequency. Therefore, the performance of single ranking metrics using term frequency and document frequency individually was examined as a feature selection method for text classification, and then the performance of combination ranking schemes using both was reviewed. Specifically, a classification experiment was conducted in an environment using two data sets (Reuters-21578, 20NG) and five classifiers (SVM, NB, ROC, TRA, RNN), and to secure the reliability of the results, 5-Fold cross-validation and t-test were applied. As a result, as a single ranking scheme, the document frequency-based single ranking metric (chi) showed good performance overall. In addition, it was found that there was no significant difference between the highest-performance single ranking and the combination ranking schemes. Therefore, in an environment where sufficient learning documents can be secured in text classification, it is more efficient to use a single ranking metric (chi) based on document frequency as a feature selection method.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.49-55
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• 2021

Recently, with the development of database, it is possible to store a lot of data generated in finance, security, and networks. These data are being analyzed through classifiers based on machine learning. The main problem at this time is data imbalance. When we train imbalanced data, it may happen that classification accuracy is degraded due to over-fitting with majority class data. To overcome the problem of data imbalance, oversampling strategy that increases the quantity of data of minority class data is widely used. It requires to tuning process about suitable method and parameters for data distribution. To improve the process, In this study, we propose a strategy to explore and optimize oversampling combinations and ratio based on various methods such as synthetic minority oversampling technique and generative adversarial networks through genetic algorithms. After sampling credit card fraud detection which is a representative case of data imbalance, with the proposed strategy and single oversampling strategies, we compare the performance of trained classifiers with each data. As a result, a strategy that is optimized by exploring for ratio of each method with genetic algorithms was superior to previous strategies.

• Korean Journal of Cognitive Science
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• v.10 no.1
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• pp.25-43
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• 1999

The focus of this study is on the elements of structure design that facilitate u user interaction with applications within cyberspace Structure design entails decisions regarding the optimal classification and hierarchical organization of information into s successively higher units. i.e .. the grouping of highly related information in the form of nodes of a site and the subsequent connection of nodes that are inter-related. The decisions are based on the designer's subjective classification framework. which is not always compatible with that of the user. We propose that the ensuing cognitive dissonance can be reduced via the employment of multiple hierarchies and cross-referencing links. Multiple hierarchies represent a single information space in terms of a number of single hierarchies. each of which represent a different perspective Cross-referencing refers to the inter-connection between the constituent hierarchies by providing a link to the alternate hierarchy for information that is most likely to be categorized in diverse manners by users with differing perspectives. In this study we conducted two empirical studies to gauge the effectiveness of multiple hierarchies and Cross-referencing links in the domain of cyber shopping malls. In the first phase. an experiment was conducted to determine how subjects classified given products with respect to two different perspectives for categorization. Experimental cyber malls were developed based on the results from the first phase to test the effectiveness of multiple hierarchies and cross-referencing links. Results show that the ease of navigation was higher for cyber malls that had implemented cross-referencing links are of greater value when used in conjunction with single hierarchical designs rather than multiple hierarchies. Users satisfaction with and ease of navigation was higher for cyber malls that had not implemented multiple hierarchies. This paper concludes with discussion of these results and their implications for designers of cyber malls.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.949-959
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• 2012

This article is to find the right size of decision trees that performs better for boosting algorithm. First we defined the tree size D as the depth of a decision tree. Then we compared the performance of boosting algorithm with different tree sizes in the experiment. Although it is an usual practice to set the tree size in boosting algorithm to be small, we figured out that the choice of D has a significant influence on the performance of boosting algorithm. Furthermore, we found out that the tree size D need to be sufficiently large for some dataset. The experiment result shows that there exists an optimal D for each dataset and choosing the right size D is important in improving the performance of boosting. We also tried to find the model for estimating the right size D suitable for boosting algorithm, using variables that can explain the nature of a given dataset. The suggested model reveals that the optimal tree size D for a given dataset can be estimated by the error rate of stump tree, the number of classes, the depth of a single tree, and the gini impurity.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.343-346
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• 2020

개체명 인식은 주어진 문장 내에서 OOV(Out of Vocaburary)로 자주 등장하는 고유한 의미가 있는 단어들을 미리 정의된 개체의 범주로 분류하는 작업이다. 최근 개체명이 문장 내에서 OOV로 등장하는 문제를 해결하기 위해 외부 리소스를 활용하는 연구들이 많이 진행되었다. 본 논문은 의미역, 의존관계 분석에 한국어 어휘지도를 이용한 자질을 추가하여 성능 향상을 보인 연구들을 바탕으로 이를 한국어 개체명 인식에 적용하고 평가하였다. 실험 결과, 한국어 어휘지도를 활용한 자질을 추가로 학습한 모델이 기존 모델에 비해 평균 1.83% 포인트 향상하였다. 또한, CRF 단일 모델만을 사용했음에도 87.25% 포인트라는 높은 성능을 보였다.

• Korean Journal of Social Welfare Studies
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• v.48 no.4
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• pp.121-156
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• 2017

The purpose of this study is to examine the recovering gambling addicts' overcoming process on their hardships of life which hamper their recovery. For this study, we had one to one depth interview with 10 participants who had overcome their hardships of life after stopping gambling. The data gathered from the interviews were analysed following the grounded theory process suggested by Strauss and Corbin. As a result, 131 concepts were constructed, and they were classified into 32 subcategories and 14 category. We presented "overcoming of the hardships of life through the acceptance of reality and re-determination" as a core subject which connects the whole concepts. Based on the research outcomes, we proposed some policy and practice intervention methods such as economic self-support program, social security service, and triangle support system by counselor-family-peer recovering addicts.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.614-616
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• 2021

The world faces difficulties in terms of eye care, including treatment, quality of prevention, vision rehabilitation services, and scarcity of trained eye care experts. However, it is difficult to develop a method for classifying various ocular diseases because the existing dataset for retinal image disclosure does not consist of various diseases found in clinical practice. We propose a method for classifying ocular diseases using the Retinal Fundus Multi-disease Image Dataset (RFMiD), a dataset published in the ISBI-2021 challenge. Our goal is to develop a robust and generalizable model for screening retinal images into normal and abnormal categories. The performance of the proposed model shows a value of 0.9782 for the test dataset as an area under the curve (AUC) score.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.142-142
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• 2010

일반적으로 기술융합이라는 용어는 IT, BT, NT 등 성격이 다른 큰 범주에서 기술간의 결합으로 인식되고 있다. 현재까지의 기술융합 연구들은 IT기술을 중심으로 한 융합과 관련 국가 정책에 관한 것이 대부분을 차지하고 있어 큰 기술 범주 위주에 국한되어 있다. 하지만 동일한 목적을 위해 수행하는 유사 기술영영에서의 기술융합 역시 기술혁신의 수단으로 간과할 수 없는 영역이다. 실제로 미국, 유럽 등의 선진국에서는 기술융합 전담기관을 신설하여 프로젝트 내의 기술간 융합에 관심을 갖고 있지만, 국내에서는 프로젝트 범위의 기술융합 가능성 및 실효성에 대한 연구가 부족한 실정이다. 이에 본 연구에서는 지식경제부에서 수행하는 가스하이드레이트 연구개발사업을 실증사례로 하여 프로젝트 범위의 기술 융합에 관하여 기술융합의 필요성, 적용가능성, 실효성에 초점을 맞추어 고찰하였다. 가스하이드레이트 개발 사업은 지식경제부 내 가스하이드레이트 개발사업단 주관으로 2005년에 시작되었으며 2014년까지 I 지역 탐사 및 시추, II 지역 탐사 및 시추, 시험생산의 3단계의 달성목표를 가지고 있다. 가스하이드레이트는 천연가스가 저온 고압 상태에서 물과 결합해 형성된 고체 에너지로 화석연료 고갈에 따라 이를 대체할 가장 유력한 청정에너지원으로 주목받고 있다. 현재 가스하이드레이트 개발사업단에서는 지구물리탐사분야 지질지화학분야 개발생산분야로 세부 기술모듈을 형성하여 목표달성을 위해 노력하고 있지만, 중과제간 교류가 부족한 상황으로 인해 목표달성을 위한 기술력의 확보 및 향후 상업생산에 대한 불확실성이 증가하고 있는 상황이다. 이와 같은 상황을 해결하기 위해서 기술개발 및 혁신의 수단인 기술융합의 필요성이 증가하고 있다. 기술혁신은 기초연구, 응용연구, 개발, 학습, 투자 등의 일련의 과정을 거쳐 경제적 성과와 사회적 영향을 만들어내는 개념으로 정의 할 수 있다. 기술혁신을 이루어내는 가장 중심적인 역할을 담당하는 기술융합은 2개 이상의 요소기술들이 결합하여 기술이 갖지 않는 새로운 기능을 발휘하는 기술혁신의 한 현상으로 정의할 수 있다. 기술융합은 21세기 초에 접어들어 급속하게 변화하는 양상을 보이며 예상보다 경제에 더 큰 영향을 미치고 있다. 가스하이드레이트 각 단계에서의 애로점을 극복하기 위한 기술혁신을 위해 지구물리탐사 지질지화학 개발생산분야간의 융합의 가능성 등을 타진해본 결과, 각 기술융합들을 기술융합 유형에 맞춰 분류할 수 있었으며 유형별 적용가능성과 기대효과 측면에서 비교분석을 수행하였다. 분석의 정밀도를 높이기 위하여 기술융합 유형에 대한 이론과 실제 가스하이드레이트 전문가들과의 설문을 통해 비교분석을 실시하였다. 가스하이드레이트 실증 사례에 대한 분석 결과, 기술융합 이론은 기존의 큰 기술범주뿐만 아니라 작은 범주에도 적용할 수 있으며, 필요성과 적용가능성, 실효성 면에서도 충분한 고찰을 통해 기술융합 이론의 적용 범위를 좁히면 더 많은 연구와 융합기술을 얻을 수 있다는 결론을 얻을 수 있다.