• Journal of Information Technology Applications and Management
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• v.14 no.2
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• pp.151-168
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• 2007

The corporate credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this study, the corporate credit rating model employs artificial intelligence methods including Neural Network (NN) and Case-Based Reasoning (CBR). At first we suggest three classification models, as partitioned neural networks, all of which convert multi-group classification problems into two group classification ones: Ordinal Pairwise Partitioning (OPP) model, binary classification model and simple classification model. The experimental results show that the partitioned NN outperformed the conventional NN. In addition, we put to use CBR that is widely used recently as a problem-solving and learning tool both in academic and business areas. With an advantage of the easiness in model design compared to a NN model, the CBR model proves itself to have good classification capability through the highest hit ratio in the corporate credit rating.

• The Journal of Fisheries Business Administration
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• v.22 no.2
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• pp.1-17
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• 1991

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.4
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• pp.141-152
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• 2011

The Internet is changing old organizational schemes, including education. A growing number of universities are creating curriculums to reflect these changes. Economic market is being opened to the international world, and in order to overcome the difficulties are demanding continuous supply of men power from university to efficiently manage human resource and implement superior e-business model. Technology innovation plays an important role in the sustainable grows of the firm in the global economy. The objective of this study is to suggest an e-business curriculum for e-business department. We will analyze what kind of people is needed for e-business, and provide appropriate curriculum for educating suitable human resources, which could be for university program. This study may contribute to the much needed systematic analysis for e-business curriculum. We prioritize e-business curriculum selected and rank their importance through an exploratory study for suggesting the undergraduate curriculum.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.2
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• pp.139-163
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• 2024

This study aims to analyze classification systems used in the public sector, collected based on legislation, and to improve the classification system for public records. From the Korean Law Information Center, 375 legislative clauses were searched, revealing about 80 classification systems. These systems were initially divided into lists, tables, and hierarchical classifications. Six types of classification system uses were proposed after combining three management types and two system functions. Among these models, classification systems used for core operations in public agencies often had the same entity as both developer and user. While systems adopted from other institutions were often modified as needed, they were predominantly used for reference tasks rather than core operations. However, in records management, crucial tasks such as record classification and disposal commonly use unmodified classification system items developed and managed by other agencies. Consequently, this study proposes that structural improvements are necessary for the record classification system. It suggests developing dedicated classification systems to support core functions or modifying existing systems and also applying records management disposal standards and guidelines to other relevant legislative provisions.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.526-533
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• 2011

Contracting is said to be a high-risk business, and a common cause of business failure is related to cash management. A contractor's financial viability depends heavily on how actual payments from an owner deviate from those defined in the contract. The paper presents a method for contractors to evaluate the punctuality and fullness of owner payments based on historical behaviour. It does this by classifying owners according to their late and incomplete payment practices. A payment profile of an owner, in the form of aging claims submitted by the contractor, is used as a basis for the method's development. Regression trees are constructed based on three predictor variables, namely, the average time to payment following a claim, the total amount ending up being paid within a certain period and the level of variability in claim response times. The Tree package in the publicly available R program is used for building the trees. The analysis is particularly useful for contractors at the pre-tendering stage, when contractors predict the likely payment scenario in an upcoming project. Based on the method, the contractor can decide whether to tender or not tender, or adjust its financial preparations accordingly. The paper is a contribution in risk management applied to claim and dispute resolution practice. It is argued that by contractors having a better understanding of owner payment behaviour, fewer disputes and contractor business failures will occur.

• JOURNAL OF ELECTRICAL WORLD
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• no.6 s.78
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• pp.18-23
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• 1983

• The Journal of Society for e-Business Studies
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• v.9 no.3
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• pp.95-112
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• 2004

Recently, extensive research has been conducted on classification systems for managing a huge amount of information with various froms in the construction industry. Classification Systems such as ISO/DIS 12006-2 and MasterFormathave been proposed as international standards, and accommodating them for Korean situation the Korean Ministry of Construction and Transportation has proposed the Unified Construction Information Classification System. As the construction industry becomes bigger and more complicated, however, the need for higher-level semantic representation of construction information has been recognized. In this study we develop a prototype ontology based upon the Unified Construction Information Classification and suggest a practical way of applying an ontology technology to the construction information systems. An ontology is a useful tool to effectively manageconstruction information and to support interoperability among heterogeneous information systems by clarifying the semantic relationship between concepts.

• The Journal of Information Systems
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• v.26 no.4
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• pp.17-37
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• 2017

Purpose Customer Loyalty is the most important factor of customer relationship management (CRM). Especially in retailing industry, where customers have many options of where to spend their money. Classifying loyal customers through customers' data can help retailing companies build more efficient marketing strategies and gain competitive advantages. This study aims to construct classification models of distinguishing the loyal customers within a Korean retailing company using data mining techniques with R language. Design/methodology/approach In order to classify retailing customers, we used combination of support vector machines (SVMs) and other classification algorithms of machine learning (ML) with the support of recursive feature elimination (RFE). In particular, we first clean the dataset to remove outlier and impute the missing value. Then we used a RFE framework for electing most significant predictors. Finally, we construct models with classification algorithms, tune the best parameters and compare the performances among them. Findings The results reveal that ML classification techniques can work well with CRM data in Korean retailing industry. Moreover, customer loyalty is impacted by not only unique factor such as net promoter score but also other purchase habits such as expensive goods preferring or multi-branch visiting and so on. We also prove that with retailing customer's dataset the model constructed by SVMs algorithm has given better performance than others. We expect that the models in this study can be used by other retailing companies to classify their customers, then they can focus on giving services to these potential vip group. We also hope that the results of this ML algorithm using R language could be useful to other researchers for selecting appropriate ML algorithms.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.133-140
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• 2018

Accurate sentiment classification is an important research topic in sentiment analysis. This study suggests an efficient classification method of Korean sentiment using word2vec and ensemble methods which have been recently studied variously. For the 200,000 Korean movie review texts, we generate a POS-based BOW feature and a feature using word2vec, and integrated features of two feature representation. We used a single classifier of Logistic Regression, Decision Tree, Naive Bayes, and Support Vector Machine and an ensemble classifier of Adaptive Boost, Bagging, Gradient Boosting, and Random Forest for sentiment classification. As a result of this study, the integrated feature representation composed of BOW feature including adjective and adverb and word2vec feature showed the highest sentiment classification accuracy. Empirical results show that SVM, a single classifier, has the highest performance but ensemble classifiers show similar or slightly lower performance than the single classifier.

• Information Systems Review
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• v.25 no.3
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• pp.27-41
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• 2023

Unlike the previous works focusing on the state-of-the-art methodologies to improve the performance of machine learning models, this study improves the 'quality' of training data used in machine learning. We propose a method to enhance the quality of training data through the processing of 'local grammar,' frequently used in corpus analysis. We collected a vast amount of unstructured corporate review text data posted by employees working in the top 100 companies in Korea. After improving the data quality using the local grammar process, we confirmed that the classification model with local grammar outperformed the model without it in terms of classification performance. We defined five factors of work engagement as classification categories, and analyzed how the pattern of reviews changed before and after the COVID-19 pandemic. Through this study, we provide evidence that shows the value of the local grammar-based automatic identification and classification of employee experiences, and offer some clues for significant organizational cultural phenomena.