• Title/Summary/Keyword: Decision-tree technique

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Development of an Expert System for Prevention of Industrial Accidents in Manufacturing Industries (제조업에서의 산업재해 예방을 위한 전문가 시스템 개발)

  • Leem Young-Moon;Choi Yo-Han
    • Journal of the Korea Safety Management & Science
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    • v.8 no.1
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    • pp.53-64
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    • 2006
  • Many researches and analyses have been focused on industrial accidents in order to predict and reduce them. As a similar endeavor, this paper is to develop an expert system for prevention of industrial accidents. Although various previous studies have been performed 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. As an initial step for the purpose of this study, 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 Answer Tree of SPSS will be used to evaluate the validity of the results of the four algorithms. The sample for this work was chosen from 10,536 data related to manufacturing industries during three years$(2002\sim2004)$ in korea. The initial sample includes a range of different businesses including the construction and manufacturing industries, which are typically vulnerable to industrial accidents.

Fault Detection of Governor Systems Using Discrete Wavelet Transform Analysis

  • Kim, Sung-Shin;Bae, Hyeon;Lee, Jae-Hyun
    • Journal of Advanced Marine Engineering and Technology
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    • v.36 no.5
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    • pp.662-673
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    • 2012
  • This study introduces a condition diagnosis technique for a turbine governor system. The governor system is an important control system to handle turbine speed in a nuclear power plant. The turbine governor system includes turbine valves and stop valves which have their own functions in the system. Because a turbine governor system is operated by high oil pressure, it is very difficult to maintain under stable operating conditions. Turbine valves supply oil pressure to the governor system for proper operation. Using the pressure variation of turbine and governor valves, operating conditions of the turbine governor control system are detected and identified. To achieve automatic detection of valve status, time-based and frequency-based analysis is employed. In this study, a new approach, wavelet decomposition, was used to extract specific features from the pressure signals of the governor and stop valves. The extracted features, which represent the operating conditions of the turbine governor system, include important information to control and diagnose the valves. After extracting the specific features, decision rules were used to classify the valve conditions. The rules were generated by a decision tree algorithm (a typical simple method for data-based rule generation). The results given by the wavelet-based analysis were compared to detection results using time- and frequency-based approaches. Compared with the several related studies, the wavelet transform-based analysis, the proposed in this study has the advantage of easier application without auxiliary features.

Computer-Aided Decision Analysis for Improvement of System Reliability

  • Ohm, Tai-Won
    • Journal of the Korea Safety Management & Science
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    • v.2 no.4
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    • pp.91-102
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    • 2000
  • Nowadays, every kind of system is changed so complex and enormous, it is necessary to assure system reliability, product liability and safety. Fault tree analysis(FTA) is a reliability/safety design analysis technique which starts from consideration of system failure effect, referred to as “top event”, and proceeds by determining how these can be caused by single or combined lower level failures or events. So in fault tree analysis, it is important to find the combination of events which affect system failure. Minimal cut sets(MCS) and minimal path sets(MPS) are used in this process. FTA-I computer program is developed which calculates MCS and MPS in terms of Gw-Basic computer language considering Fussell's algorithm. FTA-II computer program which analyzes importance and function cost of VE consists. of five programs as follows : (l) Structural importance of basic event, (2) Structural probability importance of basic event, (3) Structural criticality importance of basic event, (4) Cost-Failure importance of basic event, (5) VE function cost analysis for importance of basic event. In this study, a method of initiation such as failure, function and cost in FTA is suggested, and especially the priority rank which is calculated by computer-aided decision analysis program developed in this study can be used in decision making determining the most important basic event under various conditions. Also the priority rank can be available for the case which selects system component in FMEA analysis.

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A Feature Analysis of Industrial Accidents Using CHAID Algorithm (CHAID 알고리즘을 이용한 산업재해 특성분석)

  • Leem Young-Moon;Hwang Young-Seob
    • Journal of the Korea Safety Management & Science
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    • v.7 no.5
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    • pp.59-67
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    • 2005
  • The main objective of the statistical analysis about industrial accidents is to find out what is the dangerous factor in its own industrial field so that it is possible to prevent or decrease the number of the possible accidents by educating those who work in the fields for safety tools. However, so far, there is no technique of quantitative evaluation on danger. Almost all previous researches as to industrial accidents have only relied on the frequency analysis such as the analysis of the constituent ratio on accidents. As an application of data mining technique, this paper presents analysis on the efficiency of the CHAID algorithm to classify types of industrial accidents data and thereby identifies potential weak points in accident risk grouping.

Performance analysis and comparison of various machine learning algorithms for early stroke prediction

  • Vinay Padimi;Venkata Sravan Telu;Devarani Devi Ningombam
    • ETRI Journal
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    • v.45 no.6
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    • pp.1007-1021
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    • 2023
  • Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795 000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders, for example, strokes, can minimize the disabling effects. Thus, in this paper, we consider various risk factors that contribute to the occurrence of stoke and machine learning algorithms, for example, the decision tree, random forest, and naive Bayes algorithms, on patient characteristics survey data to achieve high prediction accuracy. We also consider the semisupervised self-training technique to predict the risk of stroke. We then consider the near-miss undersampling technique, which can select only instances in larger classes with the smaller class instances. Experimental results demonstrate that the proposed method obtains an accuracy of approximately 98.83% at low cost, which is significantly higher and more reliable compared with the compared techniques.

A Matchmaking System Adjusting the Mate-Selection Criteria based on a User's Behaviors using the Decision Tree (고객의 암묵적 이상형을 반영하여 배우자 선택기준을 동적으로 조정하는 온라인 매칭 시스템: 의사결정나무의 활용을 중심으로)

  • Park, Yoon-Joo
    • Information Systems Review
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    • v.14 no.3
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    • pp.115-129
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    • 2012
  • A matchmaking system is a type of recommender systems that provides a set of dating partners suitable for the user by online. Many matchmaking systems, which are widely used these days, require users to specify their preferences with regards to ideal dating partners based on criteria such as age, job and salary. However, some users are not aware of their exact preferences, or are reluctant to reveal this information even if they do know. Also, users' selection standards are not fixed and can change according to circumstances. This paper suggests a new matchmaking system called Decision Tree based Matchmaking System (DTMS) that automatically adjusts the stated standards of a user by analyzing the characteristics of the people the user chose to contact. AMMS provides recommendations for new users on the basis of their explicit preferences. However, as a user's behavioral records are accumulated, it begins to analyze their hidden implicit preferences using a decision tree technique. Subsequently, DTMS reflects these implicit preferences in proportion to their predictive accuracy. The DTMS is regularly updated when a user's data size increases by a set amount. This paper suggests an architecture for the DTMS and presents the results of the implementation of a prototype.

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Prediction of commitment and persistence in heterosexual involvements according to the styles of loving using a datamining technique (데이터마이닝을 활용한 사랑의 형태에 따른 연인관계 몰입수준 및 관계 지속여부 예측)

  • Park, Yoon-Joo
    • Journal of Intelligence and Information Systems
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    • v.22 no.4
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    • pp.69-85
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    • 2016
  • Successful relationship with loving partners is one of the most important factors in life. In psychology, there have been some previous researches studying the factors influencing romantic relationships. However, most of these researches were performed based on statistical analysis; thus they have limitations in analyzing complex non-linear relationships or rules based reasoning. This research analyzes commitment and persistence in heterosexual involvement according to styles of loving using a datamining technique as well as statistical methods. In this research, we consider six different styles of loving - 'eros', 'ludus', 'stroge', 'pragma', 'mania' and 'agape' which influence romantic relationships between lovers, besides the factors suggested by the previous researches. These six types of love are defined by Lee (1977) as follows: 'eros' is romantic, passionate love; 'ludus' is a game-playing or uncommitted love; 'storge' is a slow developing, friendship-based love; 'pragma' is a pragmatic, practical, mutually beneficial relationship; 'mania' is an obsessive or possessive love and, lastly, 'agape' is a gentle, caring, giving type of love, brotherly love, not concerned with the self. In order to do this research, data from 105 heterosexual couples were collected. Using the data, a linear regression method was first performed to find out the important factors associated with a commitment to partners. The result shows that 'satisfaction', 'eros' and 'agape' are significant factors associated with the commitment level for both male and female. Interestingly, in male cases, 'agape' has a greater effect on commitment than 'eros'. On the other hand, in female cases, 'eros' is a more significant factor than 'agape' to commitment. In addition to that, 'investment' of the male is also crucial factor for male commitment. Next, decision tree analysis was performed to find out the characteristics of high commitment couples and low commitment couples. In order to build decision tree models in this experiment, 'decision tree' operator in the datamining tool, Rapid Miner was used. The experimental result shows that males having a high satisfaction level in relationship show a high commitment level. However, even though a male may not have a high satisfaction level, if he has made a lot of financial or mental investment in relationship, and his partner shows him a certain amount of 'agape', then he also shows a high commitment level to the female. In the case of female, a women having a high 'eros' and 'satisfaction' level shows a high commitment level. Otherwise, even though a female may not have a high satisfaction level, if her partner shows a certain amount of 'mania' then the female also shows a high commitment level. Finally, this research built a prediction model to establish whether the relationship will persist or break up using a decision tree. The result shows that the most important factor influencing to the break up is a 'narcissistic tendency' of the male. In addition to that, 'satisfaction', 'investment' and 'mania' of both male and female also affect a break up. Interestingly, while the 'mania' level of a male works positively to maintain the relationship, that of a female has a negative influence. The contribution of this research is adopting a new technique of analysis using a datamining method for psychology. In addition, the results of this research can provide useful advice to couples for building a harmonious relationship with each other. This research has several limitations. First, the experimental data was sampled based on oversampling technique to balance the size of each classes. Thus, it has a limitation of evaluating performances of the predictive models objectively. Second, the result data, whether the relationship persists of not, was collected relatively in short periods - 6 months after the initial data collection. Lastly, most of the respondents of the survey is in their 20's. In order to get more general results, we would like to extend this research to general populations.

Loss Minimization for Distribution Network using Partial Tree Search (부분 tree 탐색을 이용한 배전계통의 손실 최소화)

  • Choi, S.Y.;Shin, M.C.;Nam, G.Y.;Cho, P.H.;Park, J.S.
    • Proceedings of the KIEE Conference
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    • 2000.07a
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    • pp.519-521
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    • 2000
  • Network reconfiguration is an operation task, and consists in the determination of the switching operations such to reach the minimum loss conditions of the distribution network. In this paper, an effective heuristic based switch scheme for loss minimization is given for the optimization of distribution loss reduction and a solution approach is presented. The solution algorithm for loss minimization has been developed based on the two stage solution methodology. The first stage of this solution algorithm sets up a decision tree which represent the various switching operations available, the second stage applies a proposed technique called cyclic best first search. Therefore, the solution algorithm of proposed method can determine on-off switch statuses for loss reduction, with a minimum computational effort.

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A Study on the Decision-Making Support System in Information Management (정보관리실(情報管理室) 경영(經營)에서의 의사결정지원(意思決定支援) 시스템에 관한 연구(硏究))

  • Lee, Woo-Bum
    • Journal of Information Management
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    • v.19 no.1
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    • pp.1-29
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    • 1988
  • The purpose of this study is to investigate a decision making support system for the effective information management. Decision making theory is reviewed and problems are discussed. A model is suggested through the computing of expected monetary value in decision tree technique. The expected monetary value is computed by 1 inking the probability theory with chance node. The selection of right expected monetary value and expected value of perfect information will make great advance the present system. It is concluded that expected monetary value and expected value of perfect information in decision tree techniques will make great aids to advance information management system.

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OLAP and Decision Tree Analysis of Productivity Affected by Construction Duration Impact Factors (공사기간 영향요인에 따른 생산성의 OLAP 분석과 의사결정트리 분석)

  • Ryu, Han-Guk
    • Journal of the Korea Institute of Building Construction
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    • v.11 no.2
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    • pp.100-107
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    • 2011
  • As construction duration significantly influences the performance and the success of construction projects, it is necessary to appropriately manage the impact factors affecting construction duration. Recently, interest in the construction industry has been rising due to the recent change in the construction legal system, and the competition among the construction companies on construction time. However, the impact factors are extremely diverse. The existing productivity data on impact factors is not sufficient to properly identify the impact factor and measure the productivity from various perspectives, such as subcontractor, time, crew, work and so on. In this respect, a multidimensional analysis by a data warehouse is very helpful in order to view the manner in which productivity is affected by impact factors from various perspectives. Therefore, this research proposes a method that effectively takes the diverse productivity data of impact factors, and generates a multidimensional analysis. Decision tree analysis, a data mining technique, is also applied in this research in order to supply construction managers with appropriate productivity data on impact factors during the construction management process.