• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2010년도 춘계학술대회
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• pp.69-70
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• 2010

최근의 물류업계에서는 관련 비즈니스간의 연계와 협업이 중요시되고 있는 실정이다. 따라서 자신의 비즈니스뿐만 아니라 협업관계에 있는 업종의 비즈니스 업무도 이해해야 하는 시대가 도래하였다. 그러나 물류전문인력양생을 위한 기존의 텍스트방식의 교육이나 세분화된 교육방식에는 한계가 따른다.

• 한국전자거래학회지
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• 제18권2호
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• pp.95-110
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• 2013

본 논문은 기계학습 기법에 기반한 웹문서 자동분류 시스템의 성능을 높이기 위한 새로운 형태의 특징가공 기법을 제안한다. 제안 기법은 하이퍼텍스트 웹문서에 대한 자동분류를 효과적으로 수행하기 위해 하이퍼링크 관계를 활용하여 특징 집합을 확장시킨다. 웹문서는 하이퍼링크 관계를 통해 서로 연결된 구조를 가지며, 그 관계는 많은 경우 연관도가 높은 문서들 간에 존재한다. 이러한 링크 정보가 분류모델의 주요 인자가 되는 특징 집합의 질을 높이는 중요한 역할을 수행할 수 있다. 제안 기법의 기본 아이디어는 워드넷 온톨로지를 기반으로 분류 대상 문서와 인접 문서들에 포함된 단어(특징)들 간의 의미적 유사도를 평가함으로써 다수의 특징들로 구성된 추상화된 개념적 특징을 생성하는 것이다. 여기서 유사도 함수는 워드넷 안에서 특징들 간의 상/하위어 관계 정보를 정량적으로 계산하게 된다. 분류모델의 구축시 추상화된 개념 특징은 일반 특징과 동일하게 간주하여 보다 정확한 분류 모델을 구축하는데 기여한다. Web-KB 문서집합을 이용한 실험을 통해 제안 기법이 기존 기법 보다 우수함을 보였다.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제14권9호
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• pp.876-883
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• 2008

정확하게 정의된 전자 카탈로그(또는 상품정보)는 전자상거래 시스템의 핵심기반이다. 전자 카탈로그의 분류정보는 전자 카탈로그 정보 구축을 위한 기반 정보이며, 전자 카탈로그를 이용하는 시스템의 질을 좌우하는 중요 정보이다. 그러나, 정보시스템의 활용이 증가함에 따라, 시스템에서 관리해야 할 전자카탈로그의 양은 대용량화되었고, 대용량 전자 카탈로그의 분류 작업은 더욱 복잡하게 되었다. 본 논문에서는 전자 카탈로그를 자동분류하기 위한 자동분류기 시스템을 설명하고 자동분류기를 활용한 기업 정보시스템의 카탈로그 관리 프로세스 개선 구축 경험 및 기업의 전자카탈로그 표준화 작업을 위한 자동분류기 활용방법을 제시한다. 더불어 향후 유사 시스템 구축에 도움이 될 수 있도록 경험으로부터 얻은 자동분류기 시스템 구축 및 활용 이슈를 제시한다.

• 정보관리연구
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• 제41권1호
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• pp.173-200
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• 2010

정부 주도하에 정책적으로 이루어지는 국책 건설사업은 국가의 사회간접자본시설에 주로 집중되며 상당한 국가예산이 투입되고 관련 분야의 전문적 역량이 총체적으로 투입되는 장기적 사업이다. 일반적으로 사회기반시설은 반영구 혹은 영구적 국가 자산으로 유지하게 되며 그에 따른 기록물 또한 해당 시설물의 수명기간 동안 보존 활용되어야 한다. 또한 국책사업 특성상 다양한 이해관계자 간의 유기적 협의체계를 지원하고 오랜 기간 동안 일관된 문서 및 기록관리체계를 유지하기 위해서는 기관별 특성을 최대한 배제한 사업 역무 위주의 표준화된 분류체계의 수립이 필요하다. 따라서 국책사업 기록물의 분류체계는 사업 자체 업무 분석을 근거로 사업 업무분류체계(WBS : Work Breakdown Structure)를 기반으로 수립되어야 한다. WBS는 프로젝트 관리(PM) 분야에서 통상적인 사업관리 기법으로 업무구분을 위해 사업초기에 수립되며, 프로젝트 목표를 달성하고 필요한 인도물을 산출하기 위하여 프로젝트 팀이 실행할 작업을 인도물 중심으로 분할한 계층 구조체계이다. 따라서 본 연구는 WBS에 기반한 국책사업 기록물 분류체계 수립에 대한 구체적인 사례와 방안을 제시함으로써 현 관리, 행정기록물 중심의 기록물 분류 체계에 대한 새로운 시각을 제시하는 데 목적이 있다.

• Asia pacific journal of information systems
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• 제29권4호
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• pp.838-855
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• 2019

Online product reviews are a vital source for companies in that they contain consumers' opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers' comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers' reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Google's pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.

• Journal of Information Processing Systems
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• 제12권1호
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• pp.169-181
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• 2016

Organizations in some industries are still hesitant to adopt the Enterprise Resource Planning (ERP) system due to its high risk of failures. This study examined how industry classification affects the successful implementation of the ERP system. To achieve this goal, we reinvestigated the existing ERP Success Model that was developed by Chung with the data from various industry sectors, since Chung validated the model only in the engineering and construction industries. In order to test to see if the Chung model can be applicable outside the engineering and construction industries, the relationships between the ERP success indicators and the critical success factors in the Chung model and those in the sample data collected from ten different industry sectors were compared and investigated. The ten industry sectors were selected based on the Global Industry Classification Standard (GICS). We found that the impact of success factors on the success of implementing an ERP system varied across industry sectors. This means that the success of ERP system implementation can be industry-specific. Thus, industry classification should be considered as another factor to help IT decision makers or top-management avoid ERP system failures when they plan to implement a new ERP system.

• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.64-79
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• 2017

Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2005년도 공동추계학술대회
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• pp.509-515
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• 2005

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved it more powerful than traditional artificial neural networks (ANNs)(Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al, 2005; Kim, 2003). The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is cost-sensitive. Therefore, it is necessary to convert the output of the classifier into well-calibrated posterior probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create probabilities (Platt, 1999; Drish, 2001). This study applies a method to estimate the probability of outputs of SVM to bankruptcy prediction and then suggests credit scoring methods using the estimated probability for bank's loan decision making.

• Journal of Information Processing Systems
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• 제10권2호
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• pp.314-333
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• 2014

When analyzing default predictions in real estate companies, the number of non-defaulted cases always greatly exceeds the defaulted ones, which creates the two-class imbalance problem. This lowers the ability of prediction models to distinguish the default sample. In order to avoid this sample selection bias and to improve the prediction model, this paper applies a minority sample generation approach to create new minority samples. The logistic regression, support vector machine (SVM) classification, and neural network (NN) classification use an imbalanced dataset. They were used as benchmarks with a single prediction model that used a balanced dataset corrected by the minority samples generation approach. Instead of using prediction-oriented tests and the overall accuracy, the true positive rate (TPR), the true negative rate (TNR), G-mean, and F-score are used to measure the performance of default prediction models for imbalanced dataset. In this paper, we describe an empirical experiment that used a sampling of 14 default and 315 non-default listed real estate companies in China and report that most results using single prediction models with a balanced dataset generated better results than an imbalanced dataset.

• 산업경영시스템학회지
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• 제46권3호
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• pp.7-14
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• 2023

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people's life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing 'heavy snow' in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.