• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.105-115
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• 2016

Ensemble classification involves combining individually trained classifiers to yield more accurate prediction, compared with individual models. Ensemble techniques are very useful for improving the generalization ability of classifiers. The random subspace ensemble technique is a simple but effective method for constructing ensemble classifiers; it involves randomly drawing some of the features from each classifier in the ensemble. The instance selection technique involves selecting critical instances while deleting and removing irrelevant and noisy instances from the original dataset. The instance selection and random subspace methods are both well known in the field of data mining and have proven to be very effective in many applications. However, few studies have focused on integrating the instance selection and random subspace methods. Therefore, this study proposed a new hybrid ensemble model that integrates instance selection and random subspace techniques using genetic algorithms (GAs) to improve the performance of a random subspace ensemble model. GAs are used to select optimal (or near optimal) instances, which are used as input data for the random subspace ensemble model. The proposed model was applied to both Kaggle credit data and corporate credit data, and the results were compared with those of other models to investigate performance in terms of classification accuracy, levels of diversity, and average classification rates of base classifiers in the ensemble. The experimental results demonstrated that the proposed model outperformed other models including the single model, the instance selection model, and the original random subspace ensemble model.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.184-192
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• 2009

CT(Culture Technology) became one of the leading industries with fast growth in its contribution to an economy not only in Korea, but also in the world. This study suggests policy direction and priority of CT in the perspective of technology standardization and its classification. As a result, this study proposes a new 'CT Classification' using the value chain concepts contrary to previous classification. In addition, we suggest standardization priority, especially, in the sectors of 'CT Distribution/Service', 'CT Marketing', 'General CT management' based on our survey research of production side. Consequently, our research suggests an important strategic bases for decision makers in CT policy and development both government and private sectors.

• Journal of Korean Society of Archives and Records Management
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• v.20 no.3
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• pp.39-54
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• 2020

Records at closed private universities are simply kept in stacks without the use of records classification systems. However, the systematic management of such records is needed as these are important records that have legal and evidential value during the litigation process. Therefore, this study intends to develop a classification system for recordkeeping at closed private universities aiming to eliminate unnecessary follow-up procedures that may occur because of the absence of a records classification system, and to develop practical tools for managing records at closed universities. To this end, Seonam University, among the 13 transfer records kept by the Korea Advancing Schools Foundation, was selected as the example for this study. The peculiarities of the closing processes and the catalogs of the transfer records were reviewed, and a business function analysis was conducted. Based on the Guidelines for Prescribing Retention Period of University Records by the National Archives and the Ministry of Education, a records classification system for the closed private universities was proposed for the Records Disposition Schedule to handle the uniqueness of closed universities.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.20 no.3
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• pp.129-147
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• 2009

The ultimate goal of classification systems is to provide tools for information management and services through collocation of information objects in similar topics. The National Research Council for Economics, Humanities, and Social Sciences(NRCS) aims to organize the research products from 23 research institutes. To manage and organize the research products effectively, the standard classification system has been developed in conjunction of users' survey and the Business Reference Model(BRM). Although the standard classification system consists of users' perspectives and the aspects of organizational functions, there are limits to apply the system into classification practices. In this study, the proposed hybrid approach is to combine a clustering approach with 1,884 keywords from the titles of research products between 2007 and 2008. The clustering approach is performed in a heuristic way according to the KDC due to the lack of digital full texts of research products. The results of this study proposed a revised standard classification system for NRCS with 16 headings and 90 sub-headings. The revised standard classification system will play an important role in managing research products effectively.

• Journal of Service Research and Studies
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• v.8 no.1
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• pp.89-102
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• 2018

The Industrial Classification is a systematic taxonomy of industrial activities and the Standard Industrial Classification is used in all country by their own a consistent classification method. Therefore, it is employed to analyze current status of industry affairs using statistical investigations in terms industrial activities for making industrial policies and to compare industrial activity among countries. Since the Second Industrial Revolution, the need for the homogenous standard of industrial classification among countries emerged as the economic and industrial exchanges between the countries have became more active. In 1940, Colin Clark who british economist divided the industry into the first (primitive), second (processed), and third (service) industries. Based on this, the United Nations Office for Statistics (UNSD) established International Standard Industry Classification (ISIC) in 1948, which most countries invoke it. ISIC(International Standard Industry Classification) and the standard industry classifications of countries have reached the present after several revisions since the enactment of the Act. In the 2000s, the standard industry classification is amended to reflect the emergence of new industries and changes in industrial structure, mainly featuring the creation and segmentation of sections in the tertiary industry domains. It also shows that primary and secondary sectors are shifting to tertiary industry. In this study, the causes of these common phenomena are systematically identified and the problems present classification systems have been analyzed. Also proposed is the direction of formation of the industrial classification system from a service economy point of view and the conceptual model of the new classification system. In the future, it is necessary to validate the proposed model through this study and to carry out various new classification system studies.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.615-643
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• 2019

Now is the time for IS scholars to demonstrate the added value of academic theory through its integration with text mining, clearly outline how to implement this for text mining experts outside of the academic field, and move towards establishing this integration as a standard practice. Therefore, in this study we develop a systematic theory-based text-mining framework (TTMF), and illustrate the use and benefits of TTMF by conducting a text-mining project in an actual business case evaluating and improving hotel service quality using a large volume of actual user-generated reviews. A total of 61,304 sentences extracted from actual customer reviews were successfully allocated to SERVQUAL dimensions, and the pragmatic validity of our model was tested by the OLS regression analysis results between the sentiment scores of each SERVQUAL dimension and customer satisfaction (star rates), and showed significant relationships. As a post-hoc analysis, the results of the co-occurrence analysis to define the root causes of positive and negative service quality perceptions and provide action plans to implement improvements were reported.

• Management & Information Systems Review
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• v.39 no.3
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• pp.1-16
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• 2020

The current tariff return system requires tax officials to calculate tax amount by themselves and pay the tax amount on their own responsibility. In other words, in principle, the duty and responsibility of reporting payment system are imposed only on the taxee who is required to calculate and pay the tax accurately. In case the tax payment system fails to fulfill the duty and responsibility, the additional tax is imposed on the taxee by collecting the tax shortfall and imposing the tax deduction on For this reason, item classifications, together with tariff assessments, are the most difficult and could pose a significant risk to entities if they are misclassified. For this reason, import reports are consigned to customs officials, who are customs experts, while paying a substantial fee. The purpose of this study is to classify HS items to be reported upon import declaration and to indicate HS codes to be recorded on import declaration. HS items were classified using the attached image in the case of item classification based on the case of the classification of items by the Korea Customs Service for classification of HS items. For image classification, CNN was used as a deep learning algorithm commonly used for image recognition and Vgg16, Vgg19, ResNet50 and Inception-V3 models were used among CNN models. To improve classification accuracy, two datasets were created. Dataset1 selected five types with the most HS code images, and Dataset2 was tested by dividing them into five types with 87 Chapter, the most among HS code 2 units. The classification accuracy was highest when HS item classification was performed by learning with dual database2, the corresponding model was Inception-V3, and the ResNet50 had the lowest classification accuracy. The study identified the possibility of HS item classification based on the first item image registered in the item classification determination case, and the second point of this study is that HS item classification, which has not been attempted before, was attempted through the CNN model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.32-41
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• 2023

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald's (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.165-172
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• 2019

Innovation activities represented by Six Sigma (6σ) led to improvements not only in manufacturing industries but also in various business fields. In the aviation industry, Six Sigma has been used as a tool of innovation since the beginning of 2000, and it has developed into a comprehensive form of innovation activity that includes various improvement tools. In this study, the innovation activities in K company that is a representative company of aviation industry are summarized in the last 10 years, and the effectiveness of the innovation tools and the performance of the tasks are also analyzed. The results of 2,091 projects over the past decade have been analyzed from various perspectives. First, we found out the tools that were used frequently at each DMAIC step, showed their frequency, and analyzed the evaluation results for the project. The project was evaluated from grade 1 (highest level) to grade 7 (lowest level) with an average grade of 4.1 for the overall project. The evaluation grades of the projects were compared and analyzed in terms of the qualifications of the leader, the roadmap for the implementation of the project, the financial effect, the size of the financial effect, the business classification, and the project execution period. These results may suggest new perspectives for companies considering or adopting innovation programs.

• KIISE Transactions on Computing Practices
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• v.22 no.10
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• pp.521-531
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• 2016

In this paper, since most business process of D-agency is being performed through some information systems, including Onnara System is a government standard operating management system, computerized accumulated in the system documentation based on, even if there is no independent job analysis system, in a judgment that can be can be tissue diagnosis, it presented a job analysis plan that leverages the existing information system. Most material is passed online in business processing between departments and between colleagues, it is returned. In situations where most information systems for such business processing is built developed, grasp the work procedures and information systems D-agency data accumulated to derive the necessary elements for job analysis quantified, and verified the validity of the element in the regression statistics.In addition, classification system (BRM, Business Reference Model) of the existing functionality that is available only Onnara System, and to establish a job analysis architecture to be able to function diagnostic departments to leverage common also in other information systems, related implement illustrating additional features of the information system, to derive a department duties value calculation formula with it, and present various job analysis plan that can actually be utilized to diagnose and derived elements department appropriate personnel.