• 한국전자거래학회지
• /
• 제26권1호
• /
• pp.107-126
• /
• 2021

전자상거래에서 상품 정보에 따른 신속하고 정확한 자동 상품 분류는 중요하다. 최근의 딥러닝 기술 발전은 자동 상품 분류에도 적용이 시도되고 있다. 성능이 우수한 딥러닝 모델개발에 있어, 학습 데이터의 품질과 모델에 적합한 데이터 전처리는 중요하다. 본 연구에서는, 텍스트 상품 데이터를 기반으로 카테고리를 자동 유추할 때, 데이터의 전처리 정도에 따른 영향력과 학습 데이터 선택 범위 영향력을 CNN모델을 사례 모델로 이용하여 비교 분석한다. 실험 분석에 사용한 데이터는 실제 데이터를 사용하여 연구 결과의 실증을 담보하였다. 본 연구가 도출한 실증 분석 및 결과는 딥러닝 상품 분류 모델 개발 시 성능 향상을 위한 레퍼런스로서 의의가 있다.

• Communications for Statistical Applications and Methods
• /
• 제16권4호
• /
• pp.675-686
• /
• 2009

In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation when the training samples include missing values or not. We consider the bootstrap confidence intervals for classification error rate when a block of observation is missing.

• 한국경영과학회:학술대회논문집
• /
• 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
• /
• pp.678-685
• /
• 2002

e-business curriculum needs to be developed reflecting requirements of the stakeholder which involve a close relation with a industrial and educational job performed in the field. In this paper, we present a new methodology for developing a market-oriented and stakeholder-led e-business curriculum using quality function deployment(QFD) and conjoint analysis. For this purpose, we analyze the data resulting from the surveyed opinions of respondents working for e-business companies, the interviewed results with professors and students and evaluate the importance of each course being involved in the curriculum with respect to the job classification (e.g.. Web planner. Web master. Web programmer. Web marketer. Web designer), and then. complete a curriculm flow diagram(CFD) considering precedence and relative difficulty among the selected courses. The e-business curriculum developed by the proposed method is useful to provide guidelines for determining courses required toward a desired job and for making a partial amendment of the curriculum.

• 한국경영과학회:학술대회논문집
• /
• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
• /
• pp.1128-1135
• /
• 2003

e-Transformation of an enterprise requires the collaboration of business processes to be suited to the business participants' purpose. To realize this collaboration, business processes should be implemented as components and the system developers could be able to reuse the components for their specific purpose. The first step of this collaboration is the discovery of exact components for business processes. A dilemma, however, is the fact that there are thousands or even millions of business processes which vary from one enterprise to another. Moreover, business processes could be decomposed into multiple levels of semantics and classified into several process areas. In general, discovery of exact business processes requires understanding of widely adopted classification schemes such as CBPC, OAGIS, or SCOR. To cope with this obstacle, business process metadata should be defined and managed regardless of specific classification schemes to support effective discovery and reuse of business processes components. In this paper, a methodology to discover business process components published in different semantic levels is proposed. The proposed methodology represents the metadata of business process components as topic maps stored in a registry and utilizes the powerful features of topic maps for process discovery. TM4J, an open-source topic map engine, is modified to support concept matching and navigation. With the implemented tool, application system developers can discover and publish the business process components effectively.

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

• 한국IT서비스학회지
• /
• 제16권3호
• /
• pp.167-183
• /
• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• 한국멀티미디어학회논문지
• /
• 제21권5호
• /
• pp.617-625
• /
• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Journal of Multimedia Information System
• /
• 제5권2호
• /
• pp.99-104
• /
• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• 한국전자거래학회지
• /
• 제6권3호
• /
• pp.181-196
• /
• 2001

This paper presents a fundamental study to establish a standardization system of the Construction Continuous Acquisition and Life-cycle Support(CALS). A state of the art in standardization of the Construction CALS is reviewed to find some defects in developing CALS system in construction industry. It is analyzed that three major parts were needed to set up a standardization system for the Construction CALS. Firstly, the range of Construction CALS standardization is set up to identifying Construction CALS and defining the standards and standardization. Secondly, the strategy to carry out more effectively in Construction CALS standardization and make the relationship of the concerned system presented here can be used to establish the Construction CALS standardization system. In addition, the spread and application device are proposed to use Construction CALS standards at public institution and construction related companies. Conclusively, a classification of the Construction CALS standards was proposed and some objects to be standardized were represented in that. Results studied in this paper will provide the primary information and basic model to develop a guideline for standardization of the Construction CALS.

• 한국전자거래학회지
• /
• 제8권3호
• /
• pp.143-164
• /
• 2003

전자상거래 규모가 시간이 지날 수록 매우 빠른 속도로 확산되어 그 활용영역이 광범위해지고 보편화되고 있다. 본 논문에서는 이러한 사회의 최근 동향을 반영하여 4년제 대학의 전자상거래학과에 적용할 수 있는 교과과정을 제안한다. 컴퓨터학, 경영학 그리고 산업공학 등 다양한 영 역에 관련되어 있는 전자상거래학의 교과과정을 제안하기 위해 현재 국내 고등교육기관에 개설된 학과를 분석하고 전자상거래학의 새로운 학문적 분류기준을 제시한다. 또한 제시된 분류기준에 따라 전자상거래학의 전공별 교과과정을 제시하며 각 과목별 개요를 기술한다.