• Title, Summary, Keyword: Decision Tree

Search Result 1,258, Processing Time 0.037 seconds

Adaptive Decision Tree Algorithm for Data Mining in Real-Time Machine Status Database (실시간 기계 상태 데이터베이스에서 데이터 마이닝을 위한 적응형 의사결정 트리 알고리듬)

  • Baek, Jun-Geol;Kim, Kang-Ho;Kim, Sung-Shick;Kim, Chang-Ouk
    • Journal of Korean Institute of Industrial Engineers
    • /
    • v.26 no.2
    • /
    • pp.171-182
    • /
    • 2000
  • For the last five years, data mining has drawn much attention by researchers and practitioners because of its many applicable domains. This article presents an adaptive decision tree algorithm for dynamically reasoning machine failure cause out of real-time, large-scale machine status database. Among many data mining methods, intelligent decision tree building algorithm is especially of interest in the sense that it enables the automatic generation of decision rules from the tree, facilitating the construction of expert system. On the basis of experiment using semiconductor etching machine, it has been verified that our model outperforms previously proposed decision tree models.

  • PDF

Multi-Interval Discretization of Continuous-Valued Attributes for Constructing Incremental Decision Tree (증분 의사결정 트리 구축을 위한 연속형 속성의 다구간 이산화)

  • Baek, Jun-Geol;Kim, Chang-Ouk;Kim, Sung-Shick
    • Journal of Korean Institute of Industrial Engineers
    • /
    • v.27 no.4
    • /
    • pp.394-405
    • /
    • 2001
  • Since most real-world application data involve continuous-valued attributes, properly addressing the discretization process for constructing a decision tree is an important problem. A continuous-valued attribute is typically discretized during decision tree generation by partitioning its range into two intervals recursively. In this paper, by removing the restriction to the binary discretization, we present a hybrid multi-interval discretization algorithm for discretizing the range of continuous-valued attribute into multiple intervals. On the basis of experiment using semiconductor etching machine, it has been verified that our discretization algorithm constructs a more efficient incremental decision tree compared to previously proposed discretization algorithms.

  • PDF

A LEARNING SYSTEM BY MODIFYING A DECISION TREE FOR CAPP

  • Lee, Hong-Hee
    • Journal of Korean Institute of Industrial Engineers
    • /
    • v.20 no.3
    • /
    • pp.125-137
    • /
    • 1994
  • Manufacturing environs constantly change, and any efficient software system to be used in manufacturing must be able to adapt to the varying situations. In a CAPP (Computer-Aided Process Planning) system, a learning capability is necessary for the CAPP system to do change along with the manufacturing system. Unfortunately only a few CAPP systems currently possess learning capabilities. This research aims at the development of a learning system which can increase the knowledge in a CAPP system. A part in the system is represented by frames and described interactively. The process information and process planning logic is represented using a decision tree. The knowledge expansion is carried out through an interactive expansion of the decision tree according to human advice. Algorithms for decision tree modification are developed. A path can be recommended for an unknown part of limited scope. The processes are selected according to the criterion such as minimum time or minimum cost. The decision tree, and the process planning and learning procedures are formally defined.

  • PDF

Dynamic Decision Tree for Data Mining (데이터마이닝을 위한 동적 결정나무)

  • Choi, Byong-Su;Cha, Woon-Ock
    • Communications for Statistical Applications and Methods
    • /
    • v.16 no.6
    • /
    • pp.959-969
    • /
    • 2009
  • Decision tree is a typical tool for data classification. This tool is implemented in DAVIS (Huh and Song, 2002). All the visualization tools and statistical clustering tools implemented in DAVIS can communicate with the decision tree. This paper presents methods to apply data visualization techniques to the decision tree using a real data set.

A Study on Machine Fault Diagnosis using Decision Tree

  • Nguyen, Ngoc-Tu;Kwon, Jeong-Min;Lee, Hong-Hee
    • Journal of Electrical Engineering and Technology
    • /
    • v.2 no.4
    • /
    • pp.461-467
    • /
    • 2007
  • The paper describes a way to diagnose machine condition based on the expert system. In this paper, an expert system-decision tree is built and experimented to diagnose and to detect machine defects. The main objective of this study is to provide a simple way to monitor machine status by synthesizing the knowledge and experiences on the diagnostic case histories of the rotating machinery. A traditional decision tree has been constructed using vibration-based inputs. Some case studies are provided to illustrate the application and advantages of the decision tree system for machine fault diagnosis.

Applying Decision Tree Algorithms for Analyzing HS-VOSTS Questionnaire Results

  • Kang, Dae-Ki
    • Journal of Engineering Education Research
    • /
    • v.15 no.4
    • /
    • pp.41-47
    • /
    • 2012
  • Data mining and knowledge discovery techniques have shown to be effective in finding hidden underlying rules inside large database in an automated fashion. On the other hand, analyzing, assessing, and applying students' survey data are very important in science and engineering education because of various reasons such as quality improvement, engineering design process, innovative education, etc. Among those surveys, analyzing the students' views on science-technology-society can be helpful to engineering education. Because, although most researches on the philosophy of science have shown that science is one of the most difficult concepts to define precisely, it is still important to have an eye on science, pseudo-science, and scientific misconducts. In this paper, we report the experimental results of applying decision tree induction algorithms for analyzing the questionnaire results of high school students' views on science-technology-society (HS-VOSTS). Empirical results on various settings of decision tree induction on HS-VOSTS results from one South Korean university students indicate that decision tree induction algorithms can be successfully and effectively applied to automated knowledge discovery from students' survey data.

Typical Classification of Rural Area Considering Settlement Environment by Decision Tree Method (정주여건을 고려한 의사결정나무기법 활용 농촌지역 유형화)

  • Bae, Seung-Jong;Kim, Dae-Sik;Eun, Sang-Kyu
    • Journal of The Korean Society of Agricultural Engineers
    • /
    • v.58 no.6
    • /
    • pp.79-92
    • /
    • 2016
  • The objective of this study is to classify the types of rural areas (138 $si{\cdot}gun$) considering settlement environment by Decision Tree Method (CHAID). The CHAID method was used for decision tree algorithm and the seven dependant variables and 5 explanatory variables were selected, respectively. By decision tree method, rural areas were finally classified into six groups through three separate processes. City area, lower area in aging rate and higher area in farmland area ratio was analyzed to be relatively rich rather than other area in the case of settlement environment index. In the future, this study will be able to utilize as a reference to the planning of rural development projects.

Basic Tongue Diagnosis Indicators for Pattern Identification in Stroke Using a Decision Tree Method

  • Lee, Ju Ah;Lee, Jungsup;Ko, Mi Mi;Kang, Byoung-Kab;Lee, Myeong Soo
    • The Journal of Korean Medicine
    • /
    • v.33 no.4
    • /
    • pp.1-8
    • /
    • 2012
  • Objectives: The purpose of this study was to specify major tongue diagnostic indicators and evaluate their significance in discriminating pattern identification subtypes in stroke patients. Methods: This study used a community based multi-center observational design. Participants (n=1,502) were stroke patients admitted to 11 oriental medical university hospitals between December 2006 and February 2010. To determine which tongue indicator affected each pattern identification, a decision tree analysis of the chi-square automatic interaction detector (CHAID) algorithm was performed. The chi-squared test was used as the criterion in splitting data with a p-value less than 0.05 for division, which is the main procedure for developing a decision tree. The minimum sample size for each node was specified as n =10, and branching was limited to two levels. Results: From the 9 tongue diagnostic indicators, 6 major tongue indicators (red tongue, pale tongue, yellow fur, white fur, thick fur, and teeth-marked tongue) were identified through the decision tree analysis. Furthermore, each pattern identification was composed of specific combinations of the 6 major tongue indicators. Conclusions: This study suggests that the 6 tongue indicators identified through the decision tree analysis can be used to discriminate pattern identification subtypes in stroke patients. However, it is still necessary to re-evaluate other pattern identification indicators to further the objectivity and reliability of traditional Korean medicine.

Classification Method of Congestion Change Type for Efficient Traffic Management (효율적인 교통관리를 위한 혼잡상황변화 유형 분류기법 개발)

  • Shim, Sangwoo;Lee, Hwanpil;Lee, Kyujin;Choi, Keechoo
    • International Journal of Highway Engineering
    • /
    • v.16 no.4
    • /
    • pp.127-134
    • /
    • 2014
  • PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.

Design of a binary decision tree using genetic algorithm for recognition of the defect patterns of cold mill strip (유전 알고리듬을 이용한 이진 트리 분류기의 설계와 냉연 흠 분류에의 적용)

  • Kim, Kyoung-Min;Lee, Byung-Jin;Lyou, Kyoung;Park, Gwi-Tae
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.6 no.1
    • /
    • pp.98-103
    • /
    • 2000
  • This paper suggests a method to recognize the various defect patterns of a cold mill strip using a binary decision tree automatically constructed by a genetic algorithm(GA). In classifying complex patterns with high similarity like the defect patterns of a cold mill stirp, the selection of an optimal feature set and an appropriate recognizer is important to achieve high recognition rate. In this paper a GA is used to select a subset of the suitable features at each node in the binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes using a linear decision function. This process is repeated at each node until all the patterns are classified into individual classes. In this way, the classifier using the binary decision tree is constructed automatically. After constructing the binary decision tree, the final recognizer is accomplished by having neural network learning sits of standard patterns at each node. In this paper, the classifier using the binary decision tree is applied to the recognition of defect patterns of a cold mill strip, and the experimental results are given to demonstrate the usefulness of the proposed scheme.

  • PDF