• Korean Journal of Child Studies
• /
• v.31 no.3
• /
• pp.83-97
• /
• 2010

Growth mixture modeling was used to identify types of changes in overt aggression from Grades 4 to 7 among a sample from the Korean Youth Panel Survey. Three discrete patterns were found to adequately explain changes of overt aggression in both boys and girls : Persistent intermediate aggression; Increasing aggression; and Decreasing aggression. Most boys (93%) fell into the Persistent intermediate aggression group and 49% of girls were found to fall into the Increasing aggression group. This suggests that prevention programs should recognize that girls are at risk of increasing aggression in their early adolescence. Multinomial logistic regression analysis shows that self-control, child abuse, peer support, and involvement with deviant peers at Grades 4 were all strongly associated with trajectory class membership. These associations did not differ by gender. These findings suggest that prevention programs should focus on the multiple risk factors of both boys and girls.

• Journal of the military operations research society of Korea
• /
• v.24 no.2
• /
• pp.170-186
• /
• 1998

The purposes of this paper are applying the IDEF(Integrated DEFinition), which was selected for standard methodology of CALS(Computer-aided Acquisition and Logistic Support) process modeling, to the acquisition process of the weapon system to activate DEfense CALS of the acquisition and management business of weapon system on Defense Planning and Management System and developing a AS-IS model which is usable and that the analysis of process problems results in. On this paper, We diagrammed the function of the acquisition and management of weapon system by using IDEF0 and presented AS-IS model. This paper focused on the development of AS-IS model which can put to practical use to find the problems of current acquisition and management process of weapon system and the embodiment of national defense CALS system. So more detailed analysis of current system and additional studies about TO-BE model would be the future research area.

• Proceedings of the Korean System Dynamics Society
• /
• 2007.11a
• /
• pp.33-48
• /
• 2007

Recently, due to the globalization of business, the efficient supply chain management(SCM) is considered as the key initiatives of business activities, and the leading logistics companies are trying to provide the differentiated 3PL services to meet their customers' needs. The domestic 3PL market scale, however, is still small and the logistics companies' competence is not good enough, so that 3PL companies need to concentrate on their logistics strategies and the government's supports and related policies are required. In this point of view, we developed the system dynamics model and forecasted middle or long-term domestic 3PL market. Through the result, we suggest the roles of government and the directions of policies to support the domestic 3PL market effectively.

• Journal of Korean Society for Quality Management
• /
• v.36 no.1
• /
• pp.72-79
• /
• 2008

The policy for credit card approval/disapproval is based on the applier's personal and financial information. In this paper, we will analyze 2 credit card approval data with several classification methods. We identify which variables are important factors to decide the approval of credit card. Our main tool is an open-source statistical programming environment R which is freely available from http://www.r-project.org. It is getting popular recently because of its flexibility and a lot of packages (libraries) made by R-users in the world. We will use most widely used methods, LDNQDA, Logistic Regression, CART (Classification and Regression Trees), neural network, and SVM (Support Vector Machines) for comparisons.

• Korean System Dynamics Review
• /
• v.9 no.1
• /
• pp.107-123
• /
• 2008

Recently, due to the globalization of business, the efficient supply chain management(SCM) is considered as the key initiatives of business activities, and the leading logistics companies are trying to provide the differentiated 3PL services to meet their customers' needs. The domestic 3PL market scale, however, is still small and the logistics companies' competence is not good enough, so that 3PL companies need to concentrate on their logistics strategies and the government's supports and related policies are required. In this point of view, we developed the system dynamics model and forecasted middle or long-term domestic 3PL market. Through the result, we suggest the roles of government and the directions of policies to support the domestic 3PL market effectively.

• Management Science and Financial Engineering
• /
• v.19 no.1
• /
• pp.1-10
• /
• 2013

Hierarchical forecasting strategy does not always outperform direct forecasting strategy. The performance generally depends on demand features. This research guides the use of the alternative forecasting strategies according to demand features. This paper developed and evaluated various classification models such as logistic regression (LR), artificial neural networks (ANN), decision trees (DT), boosted trees (BT), and random forests (RF) for predicting the relative performance of the alternative forecasting strategies for the South Korean navy's spare parts demand which has non-normal characteristics. ANN minimized classification errors and inventory costs, whereas LR minimized the Brier scores and the sum of forecasting errors.

• Journal of Information Processing Systems
• /
• v.15 no.6
• /
• pp.1489-1502
• /
• 2019

This work introduces a novel unweighted combination method (UCSS) for business failure perdition (BFP). With considering features of BFP in the age of big data, UCSS integrates the quantitative and qualitative analysis by utilizing soft set theory (SS). We adopt the conventional expert system (ES) as the basic qualitative classifier, the logistic regression model (LR) and the support vector machine (SVM) as basic quantitative classifiers. Unlike other traditional combination methods, we employ soft set theory to integrate the results of each basic classifier without weighting. In this way, UCSS inherits the advantages of ES, LR, SVM, and SS. To verify the performance of UCSS, it is applied to real datasets. We adopt ES, LR, SVM, combination models utilizing the equal weight approach (CMEW), neural network algorithm (CMNN), rough set and D-S evidence theory (CMRD), and the receiver operating characteristic curve (ROC) and SS (CFBSS) as benchmarks. The superior performance of UCSS has been verified by the empirical experiments.

• Journal of Contemporary Eastern Asia
• /
• v.15 no.2
• /
• pp.145-161
• /
• 2016

Using the Sunflower movement as a case study, this article seeks to articulate a theoretical framework to evaluate online "free spaces" as tools for political mobilization. To this end, this article conducts a thematic and content analysis of 151 posts on the official Facebook page of the Sunflower movement. Key results uncover four thematic functions among posts - expressive, informative, informative-support, and promotional - that overlap, in which the expressive theme prevails, and two thematic topics discussed by posts - damages by protesters and their ideology of freedom. I conclude that: (1) combining the logistic and thematic dimensions of posts enables a specific understanding of an online free space's political viability and anticipates the campaigns it will connect itself to; (2) the networked nature of the Sunflower movement page prompts the reconceptualization of (i) online free spaces as nodes through which various political campaigns and struggles are thematically connected by a political ideology; (ii) inactivity as a strategy where protest capital and followers accumulate to prepare and empower future mobilizations.

• Annual Conference on Human and Language Technology
• /
• 2016.10a
• /
• pp.291-295
• /
• 2016

본 논문에서는 한국어 기본 유니트 단위로 WPM을 활용한 구글 플레이 스토어 앱의 댓글 감정분석을 수행하였다. 먼저 자동 띄어쓰기 시스템을 적용한 후, 어절단위, 형태소 분석기, WPM을 각각 적용하여 모델을 생성하고, 로지스틱 회귀(Logistic Regression), 소프트맥스 회귀(Softmax Regression), 서포트 벡터머신(Support Vector Machine, SVM)등의 알고리즘을 이용하여 댓글 감정(긍정과 부정)을 비교 분석하였다. 그 결과 어절단위, 형태소 분석기보다 WPM이 최대 25%의 향상된 결과를 얻었다. 또한 분류 과정에서 로지스틱회귀, 소프트맥스 회귀보다는 SVM 성능이 우수했으며, SVM의 기본 파라미터({'kernel':('linear'), 'c':[4]})보다 최적의 파라미터를 적용({'kernel': ('linear','rbf', 'sigmoid', 'poly'), 'C':[0.01, 0.1, 1.4.5]} 하였을 때, 최대 91%의 성능이 나타났다.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.11
• /
• pp.111-116
• /
• 2017

This paper proposes a data mining approach to predicting stock price direction. Stock market fluctuates due to many factors. Therefore, predicting stock price direction has become an important issue in the field of stock market analysis. However, in literature, there are few studies applying data mining approaches to predicting the stock price direction. To contribute to literature, this paper proposes comparing single classifiers and ensemble classifiers. Single classifiers include logistic regression, decision tree, neural network, and support vector machine. Ensemble classifiers we consider are adaboost, random forest, bagging, stacking, and vote. For the sake of experiments, we garnered dataset from Korea Stock Exchange (KRX) ranging from 2008 to 2015. Data mining experiments using WEKA revealed that random forest, one of ensemble classifiers, shows best results in terms of metrics such as AUC (area under the ROC curve) and accuracy.