• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.275-284
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• 2014

Decision Tree is one of analysis techniques which conducts grouping and prediction into several sub-groups from interested groups. Researcher can easily understand this progress and explain than other techniques. Because Decision Tree is easy technique to see results. This paper uses CART algorithm which is one of data mining technique. It used 273 variables and 70094 data(2010-2011) of working environment survey conducted by Korea Occupational Safety and Health Agency(KOSHA). And then refines this data, uses final 12 variables and 35447 data. To find satisfaction factor in working environment, this page has grouped employee to 3 types (under 30 age, 30 ~ 49age, over 50 age) and analyzed factor. Using CART algorithm, finds the best grouping variables in 155 data. It appeared that 'comfortable in organization' and 'proper reward' is the best grouping factor.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.492-494
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• 2021

The decision tree technique is used as a classification technique in machine learning. However, the decision tree has a problem of consuming a lot of speed or resources due to the problem of overfitting. To solve this problem, there are bagging and boosting techniques. Bagging creates multiple samplings and models them using them, and boosting models the sampled data and adjusts weights to reduce overfitting. In addition, recently, techniques Xgboost have been introduced to improve performance. Therefore, in this paper, we collect wifi signal data for indoor positioning, apply it to the existing method and Xgboost, and perform performance evaluation through it.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.455-465
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• 2006

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, and relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze industrial waste database using data mining technique. We use k-means algorithm for clustering and C5.0 algorithm for decision tree and Apriori algorithm for association rule. We can use these outputs for environmental preservation and environmental improvement.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.241-251
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• 2006

Data mining is the method to find useful information for large amounts of data in database It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze industrial waste database using data mining technique. We use k-means algorithm for clustering and C5.0 algorithm for decision tree and Apriori algorithm for association rule. We can use these analysis outputs for environmental preservation and environmental improvement.

• Korean Journal of Childcare and Education
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• v.18 no.5
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• pp.79-99
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• 2022

Objective: This study was conducted to examine the significant factors affecting media device addiction using the data mining technique for large-scale data from the Panel Study on Korean Children Survey (PSKC). The PSKC data of this study were gathered from the elementary school students in their 10th survey (1,286 3rd grade students). Methods: The SPSS 21.0 program was used for data mining decision tree analysis, and the results are as follows. Results: First, the most important predictor of media device addiction was planning-organization which was among the sub-factors of executive function. Second, as a result of the decision tree analysis, the children with the highest probability of addiction to media devices were ones that had difficulties in planning and organizing, had mothers with a permissive parenting attitude felt difficulties in controlling behavior, and were alone at home for more than two hours a day without any adult supervision. Conclusion/Implications: The results of this study can help guide the direction of future research related to children's addiction to media devices by exploring and analyzing factors that significantly affect children's addiction to media devices.

• Journal of the Korea Safety Management & Science
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• v.7 no.5
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• pp.1-8
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• 2005

The consequences of rapid industrial advancement, diversified types of business and unexpected industrial accidents have caused a lot of damage to many unspecified persons both in a human way and a material way Although various previous studies have been analyzed to prevent industrial accidents, these studies only provide managerial and educational policies using frequency analysis and comparative analysis based on data from past industrial accidents. The main objective of this study is to find an optimal algorithm for data analysis of industrial accidents and this paper provides a comparative analysis of 4 kinds of algorithms including CHAID, CART, C4.5, and QUEST. Decision tree algorithm is utilized to predict results using objective and quantified data as a typical technique of data mining. Enterprise Miner of SAS and AnswerTree of SPSS will be used to evaluate the validity of the results of the four algorithms. The sample for this work chosen from 19,574 data related to construction industries during three years ($2002\sim2004$) in Korea.

• Journal of Korean Academy of Nursing
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• v.43 no.1
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• pp.1-10
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• 2013

Purpose: The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. Methods: A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. Results: The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. Conclusion: The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

• Clinical and Experimental Emergency Medicine
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• v.5 no.4
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• pp.230-239
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• 2018

Objective The suicide rate in South Korea is very high and is expected to increase in coming years. Intoxication is the most common suicide attempt method as well as one of the common reason for presenting to an emergency medical center. We used decision tree modeling analysis to identify predictors of risk for suicide by intentional intoxication. Methods A single-center, retrospective study was conducted at our hospital using a 4-year registry of the institute from January 1, 2013 to December 31, 2016. Demographic factors, such as sex, age, intentionality, therapeutic adherence, alcohol consumption, smoking status, physical disease, cancer, psychiatric disease, and toxicological factors, such as type of intoxicant and poisoning severity score were collected. Candidate risk factors based on the decision tree were used to select variables for multiple logistic regression analysis. Results In total, 4,023 patients with intoxication were enrolled as study participants, with 2,247 (55.9%) identified as cases of intentional intoxication. Reported annual percentages of intentional intoxication among patients were 628/937 (67.0%), 608/1,082 (56.2%), 536/1,017 (52.7), 475/987 (48.1%) from 2013 to 2016. Significant predictors identified based on decision tree analysis were alcohol consumption, old age, psychiatric disease, smoking, and male sex; those identified based on multiple regression analysis were alcohol consumption, smoking, male sex, psychiatric disease, old age, poor therapeutic adherence, and physical disease. Conclusion We identified important predictors of suicide risk by intentional intoxication. A specific and realistic approach to analysis using the decision tree modeling technique is an effective method to determine those groups at risk of suicide by intentional intoxication.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.39-45
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• 2021

This paper discusses how to handle incomplete data including missing values. Optimally processing the missing value means obtaining an estimate that is the closest to the original value from the information contained in the training data, and replacing the missing value with this value. The way to achieve this is to use a decision tree that is completed in the process of classifying information by the classifier. In other words, this decision tree is obtained in the process of learning by inputting only complete information that does not include loss values among all training data into the C4.5 classifier. The nodes of this decision tree have classification variable information, and the higher node closer to the root contains more information, and the leaf node forms a classification region through a path from the root. In addition, the average of classified data events is recorded in each region. Events including the missing value are input to this decision tree, and the region closest to the event is searched through a traversal process according to the information of each node. The average value recorded in this area is regarded as an estimate of the missing value, and the compensation process is completed.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.4
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• pp.1-9
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• 2007

in this paper, we propose the Adaptive Decision Algorithm for RFID Tag Anti-Collision. We study the RFID Tag anti-collision technique of ALOHA and the anti-collision algorithm of binary search. The existing technique is several problems; the transmitted data rate included of data, the recognition time and energy efficiency. For distinction of all tags, the Adaptive Decision algorithm identify smaller one ,each Tag_ID bit's sum of bit '1'. In other words, Adaptive Decision algorithm had standard of selection by actively, the algorithm can reduce unnecessary number of search even than the exisiting algorithm. The Adaptive Decision algorithm had performance test that criterions were reader's number of repetition and number of transmitted bits for understanding tag. We showed the good performance of Adaptive Decision algorithm better than exisiting algorithm.