• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.675-686
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• 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.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.20 no.1
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• pp.73-85
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• 2009

The school records are having lots of problems in the records management by the reason that general teachers who belongs to an elementary and a secondary school, were producing and managing all the records, they don't have any knowledge on the records management system. in this paper, tried to consider the plan of the school records classification scheme and record management system under the research on the actual condition of the management system and problem features. The first plan for managing is to complete the system of business classification scheme, the standardization of records management and develop manuals and guidelines, and the second is the range expansion of managing and reinforcing the appraisal procedures.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.842-857
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• 2023

Currently, most sentiment classification models on microblogging platforms analyze sentence parts of speech and emoticons without comprehending users' emotional inclinations and grasping moral nuances. This study proposes a hybrid sentiment analysis model. Given the distinct nature of microblog comments, the model employs a combined stop-word list and word2vec for word vectorization. To mitigate local information loss, the TextCNN model, devoid of pooling layers, is employed for local feature extraction, while BiLSTM is utilized for contextual feature extraction in deep learning. Subsequently, microblog comment sentiments are categorized using a classification layer. Given the binary classification task at the output layer and the numerous hidden layers within BiLSTM, the Tanh activation function is adopted in this model. Experimental findings demonstrate that the enhanced TextCNN-BiLSTM model attains a precision of 94.75%. This represents a 1.21%, 1.25%, and 1.25% enhancement in precision, recall, and F1 values, respectively, in comparison to the individual deep learning models TextCNN. Furthermore, it outperforms BiLSTM by 0.78%, 0.9%, and 0.9% in precision, recall, and F1 values.

• Korean Business Review
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• v.14
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• pp.63-76
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• 2001

The purchase of business services is a growing activity among finns but with little appreciation that the purchase of a service requires a modification of the decision process developed for the purchase of material goods. A taxonomy for purchasing business services is developed to create a matrix with company involvement and focus of service as dimensions. Business services are classified according to their focus on three aspects of the finn: property, people, and core business. Business services are also classified according to the degree of company involvement: high company involvement and low company involvement. A number of propositions are formulated based on insights derived from this taxonomy. The taxonomy results in six business service cells: facility support, equipment support, employee support, employee development, core business facilitator, and professional. Implications for managers considering a purchase in each category are explored. This study tests the research hypotheses delineated from the classification model and the purchasing process of business services. The strategic implications are suggested based on the findings for each cell of the classification model. This study concludes with a research agenda for further studies.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.79-95
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• 2012

As credit loan products significantly increase in most financial institutions, the number of fraudulent transactions is also growing rapidly. Therefore, to manage the financial risks successfully, the financial institutions should reinforce the qualifications for a loan and augment the ability to detect a credit loan fraud proactively. In the process of building a classification model to detect credit loan frauds, utility from classification results (i.e., benefits from correct prediction and costs from incorrect prediction) is more important than the accuracy rate of classification. The objective of this paper is to propose a new approach to building a classification model for detecting credit loan fraud based on an individual-level utility. Experimental results show that the model comes up with higher utility than the fraud detection models which do not take into account the individual-level utility concept. Also, it is shown that the individual-level utility computed by the model is more accurate than the mean-level utility computed by other models, in both opportunity utility and cash flow perspectives. We provide diverse views on the experimental results from both perspectives.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.2
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• pp.73-79
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• 2020

Building Information Modeling (BIM) tools have not only increased the use of technology in the design process, but also increased the need for more information standard systems. The object classification system consists of 327 types of construction results obtained from 25 kinds of facilities, 174 types of parts, and 207 types of construction parts. In the previous study, the property classification system was developed into 4 major classifications, 13 middle classifications, 58 small classifications (category), and 333 attribution information of roads and rivers. It is extremely difficult to input the property information according to such extensive object classification. In addition, the development of external applications such as Revit plug-ins has created a need to automate specific and repetitive tasks. Therefore, following the BIM property classification system, an attribute input program was implemented for the system to enhance the productivity and convenience of the BIM users.

• The Journal of Fisheries Business Administration
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• v.14 no.2
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• pp.1-14
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• 1983

This paper considers a classification of the 88 islands located at Kyong-nam area in Korea, using by examples of 12 components of the islands. By means of principal component analysis 2 principle components were extracted, which explained a total of 73.7% of the variance. Using an eigen variable criterion (λ>1), no further principle components were discussed. Principal component 1 and 2 explained 63.4% and 10.3% of the total variance respectively, The representation of the unrelated factor scores along the first and second principal axes produced a new information with respect to the classification of the islands. Based upon the representation, 88 islands were classified into 6 groups i. e. A, B, C, D, E, and F according to similarity of the components among them in this paper. The "Group F" belongs to a miscellaneous assortment that does not fit into the logical category. category.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.175-184
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• 2000

Recently Venture business in the area of information and communication industry is booming. Though Technology classification chart helps the potential entrepreneur through Survey paper and Internet Web Page, its service does not meet the customer demand. Hence Technology Classification system, which is proposed in this paper, will solve this problem by using virtual network among venture, technology experts and potential entrepreneurs. This system supports potential entrepreneurs' decision making for choice of venture business items by using dual client technology, and provides better services than existing systems by linking expert client and customer client, .

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.3
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• pp.1-11
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• 2007

The purpose of this study is to predict promising Location Based Services under an ubiquitous environment based on their technical characteristics, which is strongly required in rapidly changing circumstances with ubiquitous computing and Broadband Convergence Network (BcN). To begin with, this study proposes a classification scheme of ubiquitous services and defines five technology factors required for ubiquitous services. Next, this study collects a number of ubiquitous services from previous researches and classified them according to the classification scheme of ubiquitous services. And then, technical importance of the collected ubiquitous services are evaluated in terms of technology factors by using service-technology metrics. Finally, a set of important Location Based Services under ubiquitous environments and the correlations between technology factors are identified by using the evaluation results.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.167-183
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• 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.