• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.775-780
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• 2019

Techniques for predicting the future can be categorized into statistics-based and deep-run-based techniques. Among them, statistic-based techniques are widely used because simple and highly accurate. However, working-level officials have difficulty using many analytical techniques correctly. In this study, we compared the accuracy of prediction by applying multinomial logistic regression, decision tree, random forest, support vector machine, and Bayesian inference to marketing related data. The same marketing data was used, and analysis was conducted by using R. The prediction results of various techniques reflecting the data characteristics of the marketing field will be a good reference for practitioners.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.401-409
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• 2020

The purpose of this study is to develop a convergence inpatient medical service patient experience management model(IMSPEMM) that can help in the management strategy of a medical institution to create a patient-centered medical culture. Using the original data from the 2018 Medical Service Experience Survey, 593 people with medical services inpatient(MSI) over the age of 15 were analyzed. By using the decision tree model, we developed a prediction model for overall satisfaction(OS) with the inpatient medical service experience(IMSE) and the intention to recommend patient experience(RI), and were classified into 4 and 7 types. The accuracy of the model was 68.9% and 78.3%. The OS level of IMSE was the nurse area and the hospital room noise management area, and the RI decision factor was the nurse area. It is significant that the IMSPEMM for MSI was presented and confirmed that the nurse area and the noise management area of the hospital room are important factors for the inpatient experience. It is considered that further research is needed to generalize the IMSPEMM.

• Proceedings of the Korean Society for Food Science of Animal Resources Conference
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• 2001.06a
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• pp.79-79
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• 2001

• Journal of Technology Innovation
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• v.28 no.1
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• pp.31-52
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• 2020

This study was started to verify the preliminary(Ex-ante) discrimination power of the firm's high-activity using the 'Forward-looking' oriented technology appraisal model used in technology financing. The analytical firms are classified into the industry (manufacturing / non-manufacturing) and the age of company (initial / non-initial). High-activity SMEs are defined as those that achieve at least twice the average asset turnover ratio of the cluster. As a result of the discriminant model by applying C5.0 method, which is one of decision tree models, classification accuracy is more than 99% in all industries and the age of company, and it is confirmed that the discriminant power of the model is stable. As a result, the management expertise, capital involvement and funding capacity items were identified as a critical variable for the high-activity SMEs. In addition, the technology management capability and technology life cycle were also confirmed to be the items to determine high-activity SMEs in the manufacturing industry. Through this, it was possible to confirm some possibility of prior discrimination and policy utilization of high-activity SMEs by using technology appraisal items.

• Journal of Wetlands Research
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• v.21 no.4
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• pp.312-320
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• 2019

The occurrence of cyanobacteria causes problems such as oxygen depletion and increase of organic matter in the water body due to mass prosperity and death. Each year, Algae bloom warning System is issued due to the effects of summer heat and drought. It is necessary to quantitatively characterize the occurrence of cyanobacteria for proactive green algae management in the main Nakdong river. In this study, we analyzed the major influencing factors on cyanobacteria bloom using visualization and correlation analysis. A decision tree, a machine learning method, was used to quantitatively analyze the conditions of cyanobacteria according to the influence factors. In all the weirs, meteorological factors, temperature and SPI drought index, were significantly correlated with cyanobacterial cell number. Increasing the number of days of heat wave and drought block the mixing of water in the water body and the stratification phenomenon to promote the development of cyanobacteria. In the long term, it is necessary to proactively manage cyanobacteria considering the meteorological impacts.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.69-72
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• 2004

기업간의 경쟁이 심화되고 정보의 중요성에 대한 인식이 확대되어 가는 상황에서 다량의 데이터로부터 가치 있는 데이터를 추출하는 CRM 데이터 마이닝은 중대한 관심사가 아닐 수 없다. 본 연구는 데이터마이닝의 여러 활용 분야 중 고객세분화를 위해 최근 많이 사용되고 있는 데이터마이닝 기법인 로지스틱 회귀분석, 의사결정나무, 신경망 알고리즘 기법들을 비교하며, 이를 실제 아파트 고객의 데이터를 이용하여 검증하고자 한다. 따라서, 아파트 고객 세분화를 위한 데이터마이닝 수행시 기법 선택의 기준과 비교 평가의 기준을 제시하는 데 연구목적 있다.

• Korean Journal of Social Welfare Studies
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• v.49 no.4
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• pp.65-100
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• 2018

Korean Youth are facing diverse problems. For-instance Korean youth are even called '7 given-up generation' which indicates that they gave up marriage, giving birth, social relationship, housing, dream and the hope. From this point, the study concludes that the influential factors of the alcohol problems of youth should be studied based on the eco social perspectives. And it adopted data-mining methods, using SAS-Enterprise Miner for the analysis, targeting 2538 youths. Specifically, the study analyzed and chose the most predictable model using decision tree analysis, artificial neural network and logistic analysis. As the result, the study found that gender, age, smoking, spouse, family-number, jobsearching and economic participation are statistically significant determinants of alcohol problems of youth. Precisely, those who are male, younger, have the spouse, have less family number, searching jobs, have more income and have the job were more prone to have the alcohol problems. Based on the result, this study proposed the addiction problems targeting youth and etc. and expect to have the contribution on implementing procedures for the alcohol problems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.93-103
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• 2019

This study analyzed the impact of smartphone usage on walking speed during walking on two pedestrian walkways in Daejeon Metropolitan City. For the analysis, the video data about the actual use of smartphone was acquired and the walking speed was calculated based on the walking density of the pedestrian Level Of Service(LOS) presented in the Road Capacity Manual. Multiple regression analysis and decision tree using machine learning were used to analyze the impact of smartphone usage on walking speed, and as the explanatory variables, gender, disable smartphone, use of smartphone using auditory function, use of smartphone using visual function, LOS A, LOS B, LOS C were adopted. The result showed that LOS C had the highest impact on walking speed change and the women's group using their visual function was founded to have the slowest walking speed in LOS C. In particular, the author found that walking speed significantly decreased in the case of use of visual function rather than listening to music or the hearing on the phone.

• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.414-427
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• 2015

Most Studies for office market have focused on large-scale office buildings. There is, if any, a little research for small and medium-sized office buildings due to the lack of data. This study uses the self-searched and established 1,056 data in Gangnam-Gu, and estimates the data by not only linear regression model, but also data mining methods. The results provide investors with various information of price determinants, for small and medium-sized office buildings, comparing with large-scale office buildings. The important variables are street frontage condition, zoning of commercial area, distance to subway station, and so on.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.3
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• pp.269-275
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• 2004

Geographic data have widely been applied in different areas including landuse, city planning and management, environment, disaster management and even daily use of citizens. Since geographic data have been built individually using different methods, many problems such as data inconsistency, duplicated investment, and confusion in decision making have arisen. Thus, the necessity of national framework database that can be shared by different areas has increased. As a result, eight fields of the framework database were defined by NGIS Law and 19 detailed items were selected. This study used the AHP (Analytical Hierarchy Process) and the decision tree to evaluate the relative importance of the items (eg. roads, railroads, coastline, surveying control points, and etc.) and presented the groups classified according to the priorities of the items. The result of this study is believed to contribute to effective budget planning for building national framework database.