의사결정나무 기법을 이용한 노인들의 자살생각 예측모형 및 의사결정 규칙 개발

김덕현,유동희,정대율 한국정보시스템학회 2019 情報시스템硏究 Vol.28 No.3

Purpose The purpose of this study is to develop a prediction model and decision rules for the elderly's suicidal ideation based on the Korean Welfare Panel survey data. By utilizing this data, we obtained many decision rules to predict the elderly's suicide ideation. Design/methodology/approach This study used classification analysis to derive decision rules to predict on the basis of decision tree technique. Weka 3.8 is used as the data mining tool in this study. The decision tree algorithm uses J48, also known as C4.5. In addition, 66.6% of the total data was divided into learning data and verification data. We considered all possible variables based on previous studies in predicting suicidal ideation of the elderly. Finally, 99 variables including the target variable were used. Classification analysis was performed by introducing sampling technique through backward elimination and data balancing. Findings As a result, there were significant differences between the data sets. The selected data sets have different, various decision tree and several rules. Based on the decision tree method, we derived the rules for suicide prevention. The decision tree derives not only the rules for the suicidal ideation of the depressed group, but also the rules for the suicidal ideation of the non-depressed group. In addition, in developing the predictive model, the problem of over-fitting due to the data imbalance phenomenon was directly identified through the application of data balancing. We could conclude that it is necessary to balance the data on the target variables in order to perform the correct classification analysis without over-fitting. In addition, although data balancing is applied, it is shown that performance is not inferior in prediction rate when compared with a biased prediction model.

Towards A Better Approach for Mastering Industrial Risks From Modeling Accidental Process to Integrating Safety Analysis Techniques Supporting the Identification of Intelligent Safety Decision

Dahmane Djamal,Bahmed Lylia,Bensiali Abdelkarim 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.3

This paper introduces a research aiming at the development of a new approach to mastering industrial risks and prevent accident scenario. Starting from modeling and analyzing accidental process to understand the causes of accidents using quantitative risk assessment and reliability, they are essential issues in modern safety to make reliable decision, and it is new approach used for risk management to identify accident scenarios that may occur at their facilities which are sources of damage. Risk assessment of safety instrumented systems is approaches designed primarily to reduce the existing risk inherent in engineering system to a level considered tolerable and maintain it over time. In this study, the reliability of quantitative risk assessment using fuzzy sets based on event tree analysis and layer of protection analysis is the model proposed to deal with inaccuracy and uncertainty of data, The model proposed to determine the severity of the scenario and determine the safety integrity level SIL using Fuzzy Sets theory. The results which have got by this model is more motivate to deal with uncertainty of which considered as complementary for logical and arithmetic computation. As the accident is a chain of failure events, each related to its (causal) event or events, the early detection and diagnosis of faults in processes is very important, we use Fault Tree Analysis to show the possible malfunctions by enumerating the suspect components and their respective failure modes. Fault diagnosis when error occurs is performed by engineers and analysts performing extensive examination of all data feedback from the system and decisions are made based on threshold values by using fault trees. Since those decision-making tasks are safety critical and must be done promptly, the engineers who manually analyze the data are facing time challenge. To automate this process, we present in this approach to use decision trees to capture the contents of fault trees and detect faults by running the telemetry data through the decision trees in real time. Decision trees are the binary trees built from data samples and can classify the objects into different classes, the decision trees can classify different fault events or normal events. Given a set of data samples, decision trees can be built and trained, and then by running the new data through the trees, classification and prediction can be made.

의사결정 학습모형에 기초한 의사결정나무 활동이 유아의 의사결정력과 조망수용능력에 미치는 영향

최지영 ( Jiyoung Choi ) 아시아문화학술원 2018 인문사회 21 Vol.9 No.5

본 연구의 목적은 의사결정 학습모형에 기초한 의사결정나무 활동이 유아의 의사결정력과 조망수용능력에 미치는 영향을 분석하고자 하였다. 연구대상은 만 5세 유아 37명으로, 연구도구는 박지영(2012)이 개발한 유아 의사결정력 검사와 이수기(2005)가 사용한 조망수용능력 검사 도구를 사용하였으며, SPSS 21.0 프로그램을 활용하여 t-test를 실시하였다. 연구결과 의사결정 학습모형에 기초한 의사결정나무 활동을 실시한 실험집단의 유아들이 통제집단 유아들보다 의사결정력과 조망수용능력에서 통계적으로 유의미하게 향상된 것으로 나타났다. 이는 의사결정 학습모형에 기초한 의사결정나무 활동이 유아의 의사결정력과 조망수용능력에 효과적임을 시사하고 있으며, 유아를 위한 의사결정력 증진을 위한 교수학습방법의 현장 적용 가능성을 확인하였다는데 교육적 의의가 있다. The purpose of this study was to examine the effects of decision tree activity based on decision-making learning model of young children’s decision making ability and perspective-taking ability. The subjects of this research were a total of 37 five-year-old children. The research data were collected through the test of decision making ability (Park, 2015) and the test of perspective-taking ability (Lee, 2015). The collected data were analyzed by t-test using SPSS 21.0 program. The results of this study were as follows: the experimental group which conducted the decision tree activity based on decision-making learning model showed significantly higher improvement of young children’s decision making ability and perspective-taking ability. This implies that the decision tree activity based on decision-making learning model for young children is effective in decision making ability and perspective-taking ability.

터키 경제 불확실성에 관한 예측 모델 비교 : 선형회귀트리, 의사결정트리, 인공신경망 모델

양오석(Oh Suk Yang) 한국이슬람학회 2021 한국이슬람학회논총 Vol.31 No.1

The main purpose of this article is to identify the optimal predictive model for predicting the economic uncertainty of emerging economies in Turkey. To do this, after obtaining a comprehensive set of related data, the final prediction model was selected by comparing predictive power between three models, such as a linear regression tree model, a decision tree model (bagging tree, random forest), and a neural network model, through a cross-validation technique. Key variables in a model include exchange rates, interest rates, stock markets, bond markets, economic atmosphere, counterparty risk, emerging country risk, and strategic import dependence. A series of economic indicators data for quantitative analysis used data from specialized database companies such as Thomson Reuters Datastream, Worldscope, Bankscope, and Osiris. The main finding is that the predictive power of the random forest model showed a relatively low MSE value compared to other predictive models, indicating its suitability as an optimal prediction model.

Performance Comparison Analysis of Artificial Intelligence Models for Estimating Remaining Capacity of Lithium-Ion Batteries

김규하,정병수,이상현 국제문화기술진흥원 2023 International Journal of Advanced Culture Technolo Vol.11 No.3

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

경영분석지표와 의사결정나무기법을 이용한 유상증자 예측모형 개발

김명균(Myeong-Kyun Kim),조윤호(Yoonho Cho) 한국지능정보시스템학회 2012 지능정보연구 Vol.18 No.4

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms’ growth, profitability, stability, activity, productivity, etc., and regularly report the firms’ financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction model

원자력 발전소 사고 예측 모형과 병합한 최적 운행중지 결정 모형

양희중 한국산업경영시스템학회 2018 한국산업경영시스템학회지 Vol.41 No.4

Recently, the continuing operation of nuclear power plants has become a major controversial issue in Korea. Whether to continue to operate nuclear power plants is a matter to be determined considering many factors including social and political factors as well as economic factors. But in this paper we concentrate only on the economic factors to make an optimum decision on operating nuclear power plants. Decisions should be based on forecasts of plant accident risks and large and small accident data from power plants. We outline the structure of a decision model that incorporate accident risks. We formulate to decide whether to shutdown permanently, shutdown temporarily for maintenance, or to operate one period of time and then periodically repeat the analysis and decision process with additional information about new costs and risks. The forecasting model to predict nuclear power plant accidents is incorporated for an improved decision making. First, we build a one-period decision model and extend this theory to a multi-period model. In this paper we utilize influence diagrams as well as decision trees for modeling. And bayesian statistical approach is utilized. Many of the parameter values in this model may be set fairly subjective by decision makers. Once the parameter values have been determined, the model will be able to present the optimal decision according to that value.

Fast incremental learning of logistic model tree using least angle regression

Lee, Sudong,Jun, Chi-Hyuck Elsevier 2018 expert systems with applications Vol.97 No.-

<P><B>Abstract</B></P> <P>Expert and intelligent systems understand the underlying information behind the data by relying on a wide range of machine learning techniques. The interpretation of machine learning models is often the key to success in research areas such as business, finance, medical and health science, and bioinformatics; such research areas demand human understanding of the obtained model. The logistic model tree (LMT) algorithm is a popular classification method that combines a decision tree and logistic regression models. The combination of two complementary algorithms produces an accurate and interpretable classifier by combining the advantages of both logistic regression and tree induction. However, LMT has the disadvantage of high computational cost, which makes the algorithm undesirable in practice. In this paper, we propose an efficient method to learn the logistic regression models in the tree. We employ least angle regression to update the regression model in LogitBoost so that the algorithm efficiently learns sparse logistic regression models composed of relevant input variables. We compare the performance of our proposed method with the original LMT algorithm using 14 benchmark datasets and show that the training time dramatically decreases while the accuracy is preserved. Our proposed algorithm is not only accurate and intuitively interpretable but also computationally efficient. It helps users in making the best possible use of the data that are included in expert and intelligent systems.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Computational efficiency of logistic model tree (LMT) algorithm is improved. </LI> <LI> An efficient boosting method for sparse logistic regression learning is proposed. </LI> <LI> The proposed method employs least angle regression to incorporate variable selection into the boosting process. </LI> <LI> Experimental results on 14 datasets to compare the proposed method with the original LMT algorithm are presented. </LI> </UL> </P>

Optimal Decision Tree를 이용한 Unseen Model 추정방법

김성탁,김회린,Kim Sungtak,Kim Hoi-Rin 대한음성학회 2003 말소리 Vol.45 No.-

Decision tree-based state tying has been proposed in recent years as the most popular approach for clustering the states of context-dependent hidden Markov model-based speech recognition. The aims of state tying is to reduce the number of free parameters and predict state probability distributions of unseen models. But, when doing state tying, the size of a decision tree is very important for word independent recognition. In this paper, we try to construct optimized decision tree based on the average of feature vectors in state pool and the number of seen modes. We observed that the proposed optimal decision tree is effective in predicting the state probability distribution of unseen models.

Deciding the Optimal Shutdown Time Incorporating the Accident Forecasting Model

Hee Joong Yang 한국산업경영시스템학회 2018 한국산업경영시스템학회지 Vol.41 No.4

Recently, the continuing operation of nuclear power plants has become a major controversial issue in Korea. Whether to continue to operate nuclear power plants is a matter to be determined considering many factors including social and political factors as well as economic factors. But in this paper we concentrate only on the economic factors to make an optimum decision on operating nuclear power plants. Decisions should be based on forecasts of plant accident risks and large and small accident data from power plants. We outline the structure of a decision model that incorporate accident risks. We formulate to decide whether to shutdown permanently, shutdown temporarily for maintenance, or to operate one period of time and then periodically repeat the analysis and decision process with additional information about new costs and risks. The forecasting model to predict nuclear power plant accidents is incorporated for an improved decision making. First, we build a one-period decision model and extend this theory to a multi-period model. In this paper we utilize influence diagrams as well as decision trees for modeling. And bayesian statistical approach is utilized. Many of the parameter values in this model may be set fairly subjective by decision makers. Once the parameter values have been determined, the model will be able to present the optimal decision according to that value.