• Journal of the Korea Safety Management & Science
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• v.8 no.3
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• pp.1-9
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• 2006

Recently data mining techniques have been used for analysis and classification of data related to industrial accidents. The main objective of this study is to compare algorithms for data analysis of industrial accidents and this paper provides an optimal predicting model of 5 kinds of algorithms including CHAID, CART, C4.5, LR (Logistic Regression) and NN (Neural Network) with ROC chart, lift chart and response threshold. Also, this paper provides an approximation model for an optimal predicting model based on NN. The approximation model provided in this study can be utilized for easy interpretation of data analysis using NN. This study uses selected ten independent variables to group injured people according to a dependent variable in a way that reduces variation. In order to find an optimal predicting model among 5 algorithms, a retrospective analysis was performed in 67,278 subjects. The sample for this work chosen from data related to industrial accidents during three years ($2002\;{\sim}\;2004$) in korea. According to the result analysis, NN has excellent performance for data analysis and classification of industrial accidents.

• 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.

• Journal of Korean Society of Transportation
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• v.31 no.3
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• pp.19-32
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• 2013

The study aims to find the characteristics of children's crossing behavior on crosswalk in school zones. It considers accident occurrence and physical form of school zones. Seven elementary school zones were investigated. Using data collected by field observation and video recording, statistical analysis, CHAID algorithm analysis, and pattern analysis were performed. As a result, it was found that children's waiting, attention and distraction were related to the accident occurrence. While 69.1% children showed waiting-before-crossing behavior in low-accident occurrence crosswalk, 83.6% children showed non waiting-before-crossing behavior in high-accident occurrence crosswalk. Moreover, the ratio of waiting, attention behavior was found to be higher when the width of the crosswalk was wide and the distance from the school's entrance to the crosswalk was long. These research findings showed that children's behavior-oriented approach was required to improve safety in school zone.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.28-33
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• 2022

This study aims to investigate factors that affect elderly poverty based on a comprehensive and universal perspective, suggesting some alternatives for improving the poverty rate of the elderly. The comprehensive and universal approach to the poverty of the aged that this study attempts can give a better understanding of the elderly poverty beyond the contribution of the existing literature, with the research model including individual, family, labor, and income factors as the causes of old-age poverty from the comprehensive and universal perspective on the causes of poverty of the elderly. In addition, the study attempts to input variants of variables into the equation for the causes of elderly poverty by using panel data from the 8th Korean Retirement and Income Study. This study employs decision tree analysis to determine the cause of the poverty of the elderly using CHAID. The decision tree analysis shows that the most vital variable affecting elderly poverty is making income. For the poor elderly without earned income, public pensions, educational careers, and residential areas influence elderly poverty, but for the poor elderly with earned income, wage earners and gender are variables that affect poverty. This study suggests some alternatives to improve the poverty rate of the aged. The government should create a better working environment such as senior re-employment for old people to be able to participate in economic activities, improve public pension or social security for workers with unfavorable conditions for public security of old age, and give companies that create employment of the aged diverse incentives.

• Journal of Physiology & Pathology in Korean Medicine
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• v.37 no.6
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• pp.172-177
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• 2023

In order to deduce the pathogenic factor(PF) diagnosis logic of underlying in pattern identification of Korean medicine, 2,072 cases of DSOM(Diagnosis System of Oriental Medicine) data from May 2005 to April 2022 were collected and analyzed by means of decision tree model(DTM). The entire data were divided into training data and validation data at a ratio of 7:3. The CHAID algorithm was used for analysis of DTM, and then validity was tested by applying the validation data. The decision rules of items and pathways determined from the diagnosis data of Qi Deficiency, Blood Deficiency, Yin Deficiency and Yang Deficiency Pathogenic Factor of DSOM were as follows. Qi Deficiency PF had 7 decision rules and used 5 questions: Q124, Q116a, Q119, Q119a, Q55. The primary indicators(PI) were 'lack of energy' and 'weary of talking'. Blood deficiency PF had 7 decision rules and used 6 questions: Q113, Q84, Q85, Q114, Q129, Q130. The PI were 'numbness in the limbs', 'dizziness when standing up', and 'frequent cramps'. Yin deficiency PF had 3 decision rules and used 2 questions: Q144 and Q56. The PI were 'subjective heat sensation from the afternoon to night' and 'heat sensation in the limbs'. Yang deficiency PF had 3 decision rules and used 3 questions: Q55, Q10, and Q102. The PI were 'sweating even with small movements' and 'lack of energy'. Conclusively, these rules and symptom information to decide the Qi·Blood·Yin·Yang Deficiency PF would be helpful for Korean medicine diagnostics.

• Journal of Korean Society for Quality Management
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• v.34 no.2
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• pp.129-135
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• 2006

In the advance of computer technology, it is possible to keep all the related informations for monitoring equipments in control and huge amount of real time manufacturing data in a data base. Thus, the statistical analysis of large data sets with hundreds of thousands observations and hundred of independent variables whose some of values are missing at many observations is needed even though it is a formidable computational task. A tree structured approach to classification is capable of screening important independent variables and their interactions. In a Six Sigma project handling large amount of manufacturing data, one of the goals is to screen vital few variables among trivial many variables. In this paper we have reviewed and summarized CART, C4.5 and CHAID algorithms and proposed a simple method of screening vital few variables by selecting common variables screened by all the three algorithms. Also how to develop a logistics regression model on a large data set is discussed and illustrated through a large finance data set collected by a credit bureau for th purpose of predicting the bankruptcy of the company.

• Proceedings of the Safety Management and Science Conference
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• 2006.11a
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• pp.293-297
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• 2006

Recently most universities are suffering from students leaving their majors. In order to make a countermeasure for reducing major separation rate, many universities are trying to find a proper solution. As a similar endeavor, this paper uses decision tree algorithm which is one of the data mining techniques which conduct grouping or prediction into several sub-groups from interested groups. This technique can analyze a feature of type on students leaving their majors. The dataset consists of 5,115 features through data selection from total data of 13,346 collected from a university in Kangwon-Do during seven years(2000.3.1 $\sim$ 2006.6.30). The main objective of this study is to evaluate performance of algorithms including CHAID, CART and C4.5 for classification of students leaving their majors with ROC Chart, Lift Chart and Gains Chart. Also, this study provides values about accuracy, sensitivity, specificity using classification table. According to the analysis result, CART showed the best performance for classification of students leaving their majors.

• Journal of the Korea Safety Management & Science
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• v.8 no.5
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• pp.17-25
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• 2006

Nowadays most colleges are confronting with a serious problem because many students have left their majors at the colleges. In order to make a countermeasure for reducing major separation rate, many universities are trying to find a proper solution. As a similar endeavor, the objective of this paper Is to find a predicting model of students leaving their majors. The sample for this study was chosen from a university in Kangwon-Do during seven years(2000.3.1 $\sim$ 2006. 6.30). In this study, the ratio of training sample versus testing sample among partition data was controlled as 50% : 50% for a validation test of data division. Also, this study provides values about accuracy, sensitivity, specificity about three kinds of algorithms including CHAID, CART and C4.5. In addition, ROC chart and gains chart were used for classification of students leaving their majors. The analysis results were very informative since those enable us to know the most important factors such as semester taking a course, grade on cultural subjects, scholarship, grade on majors, and total completion of courses which can affect students leaving their majors.

• Journal of Korean Society for Quality Management
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• v.42 no.1
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• pp.71-79
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• 2014

Purpose: The purpose of this study is to develop the methods for evaluating patients' queue environment using decision tree and queueing theory. Methods: This study uses CHAID decision tree and M/G/1 queueing theory to estimate pain point and patients waiting time for medical service. This study translates hospital physical data process to logical process to adapt queueing theory. Results: This study indicates that three nodes of the system has predictable problem with patients waiting time and can be improved by relocating patients to other nodes. Conclusion: This study finds out three seek points of the hospital through decision tree analysis and substitution nodes through the queueing theory. Revealing the hospital patients' queue environment, this study has several limitations such as lack of various case and factors.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.