• Journal of the Korea Safety Management & Science
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• v.5 no.3
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• pp.107-119
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• 2003

Expectation and interest about e-CRM are rising for more efficient customer management in on-line including electronic commerce. The decision-making tree can be used usefully as the data mining technology for e-CRM. In this paper, the representative decision making techniques, CART, C4.5, CHAID analyzed the differences in personalization point of view with actuality customer data through an experiment. With these analysis data, it is proposed a new decision-making tree system that has big advantage in personalization techniques. Through new system, it can get following advantage. First, it can form superior model more qualitatively in personalization by adding individual's weight value. Second it can supply information personalized more to customer. Third, it can have high position about customer's loyalty than other site of similar types of business. Fourth, it can reduce expense that cost marketing and decision-making. Fifth, it becomes possible that know that customer through smooth communication with customer who use personalized service wants and make from goods or service's quality to more worth thing.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.68-80
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• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.4
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• pp.187-194
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• 2008

Recently, the constructions of widening bridges or new bridges are often undergoing as a part of road widening because traffic volumes are rapidly increasing caused by fast-growing population and urbanization. But in general, there is no rational decision process and specification to justify the validity of the bridge widening. Moreover, there are also numerous events including various uncertainties involved in widening bridges. In this paper, therefore, a decision making model is proposed for widening bridges using decision tree based on quantitative LCC analysis considering a variety of uncertainties for the rational and practical approach to a quantitative decision making for alternatives.

• Proceedings of the KSR Conference
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• 2006.05b
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• pp.1179-1187
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• 2006

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.156-166
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• 2002

Decision tree induction is a kind of useful machine learning approach for extracting classification rules from a set of feature-based examples. According to the partitioning style of the feature space, decision trees are categorized into univariate decision trees and multivariate decision trees. Due to observation error, uncertainty, subjective judgment, and so on, real-world data are prone to contain some errors in their feature values. For the purpose of making decision trees robust against such errors, there have been various trials to incorporate fuzzy techniques into decision tree construction. Several researches hove been done on incorporating fuzzy techniques into univariate decision trees. However, for multivariate decision trees, few research has been done in the line of such study. This paper proposes a fuzzy decision tree induction method that builds fuzzy multivariate decision trees named fuzzy oblique decision trees, To show the effectiveness of the proposed method, it also presents some experimental results.

• IE interfaces
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• v.24 no.4
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• pp.408-419
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• 2011

R&D is a foundation for new business chance and productivity improvement leading to enormous expense and a long-term multi-step process. During the R&D process, decision-makers are confused due to the various future uncertainties that influence economic and technical success of the R&D projects. For these reasons, several decision-making models for R&D project investment have been suggested; they are based on traditional methods such as Discounted Cash Flow (DCF), Decision Tree Analysis (DTA) and Real Option Analysis (ROA) or some fusion forms of the traditional methods. However, almost of the models have constraints in practical use owing to limits on application, procedural complexity and incomplete reflection of the uncertainties. In this study, to make the constraints minimized, we propose a new model named Real Option Decision Tree Model which is a conceptual combination form of ROA and DTA. With this model, it is possible for the decision-makers to simulate the project value applying the uncertainties onto the decision making nodes.

• Proceedings of the Korea Association of Information Systems Conference
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• 2002.11a
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• pp.289-312
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• 2002

This study suggests the Link weight analysis approach to choose input variables and an integrated model to make more accurate bankruptcy prediction model. the Link weight analysis approach is a method to choose input variables to analyze each input node's link weight which is the absolute value of link weight between an input nodes and a hidden layer. There are the weak-linked neurons elimination method, the strong-linked neurons selection method in the link weight analysis approach. The Integrated Model is a combined type adapting Bagging method that uses the average value of the four models, the optimal weak-linked-neurons elimination method, optimal strong-linked neurons selection method, decision-making tree model, and MDA. As a result, the methods suggested in this study - the optimal strong-linked neurons selection method, the optimal weak-linked neurons elimination method, and the integrated model - show much higher accuracy than MDA and decision making tree model. Especially the integrated model shows much higher accuracy than MDA and decision making tree model and shows slightly higher accuracy than the optimal weak-linked neurons elimination method and the optimal strong-linked neurons selection method.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.829-839
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• 2013

This study is a descriptive research study with the purpose of predicting and comparing factors of depression affecting residents in a metropolitan city by using logistic regression analysis and decision-making tree analysis. The subjects for the study were 462 residents ($20{\leq}aged{\angle}65$) in a metropolitan city. This study collected data between October 7, 2011 and October 21, 2011 and analyzed them with frequency analysis, percentage, the mean and standard deviation, ${\chi}^2$-test, t-test, logistic regression analysis, roc curve, and a decision-making tree by using SPSS 18.0 program. The common predicting variables of depression in community residents were social dysfunction, perceived physical symptom, and family support. The specialty and sensitivity of logistic regression explained 93.8% and 42.5%. The receiver operating characteristic (roc) curve was used to determine an optimal model. The AUC (area under the curve) was .84. Roc curve was found to be statistically significant (p=<.001). The specialty and sensitivity of decision-making tree analysis were 98.3% and 20.8% respectively. As for the whole classification accuracy, the logistic regression explained 82.0% and the decision making tree analysis explained 80.5%. From the results of this study, it is believed that the sensitivity, the classification accuracy, and the logistics regression analysis as shown in a higher degree may be useful materials to establish a depression prediction model for the community residents.

• Journal of Korea Water Resources Association
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• v.35 no.5
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• pp.597-609
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• 2002

This study suggests a methodology of hydrologic decision making for the establishment of a standard of drought management from the drought analysis by the past drought history and for the drought monitoring and management according to drought processing. The construction and analysis of a decision tree diagram are performed and the step by step plan according to drought severity is suggested. Say, the decision tree diagram is constructed by the transition probability and quantity of monthly precipitation. Then the drought processing is investigated by the analysis of diagram and the 3-step of drought notice, drought warning, and emergency plan are established. The suggested methology in this study can be used for the other area and the decision tree diagram be used by changing the diagram according to the utilization purposes. Also, the choice of monthly PDSI class and precipitation analysis can be performed by the continuous data supplement. And so, a new standard value by the modified diagram is provided and the continuous drought management will be possible.

• Journal of Korean Society of Rural Planning
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• v.28 no.4
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• pp.1-9
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• 2022

Rural decline due to the decrease of the local population is an inevitable phenomenon, and a vicious cycle has been formed between a lack of basic living services and a population decrease in rural areas. Therefore, the study aims to derive the migration decision-making characteristics based on basic living service infrastructure data in rural areas. To do this, the population change over the past 20 years was categorized into six types, and the relationship between the classified population change types and the number of basic living service infrastructures was analyzed using decision tree analysis. Of the total 3,501 regions, 801 regions were the continuous decline type, of which 740 were rural areas. On the other hand, among 569 regions that were the continuous increase type, 401 regions were urban areas, confirming the population imbalance between rural and urban areas. As a result of the decision tree analysis on the relationship between population change types and the distribution of basic living service infrastructure, the number of daycare centers was derived as an important variable to classify the continuous increase type. Hospitals, parks, and public transportation were also found to be major basic living services affecting the classification of population change types.