• Title, Summary, Keyword: Decision Tree

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Waste Database Analysis Joined with Local Information Using Decision Tree Techniques

  • Park, Hee-Chang;Cho, Kwang-Hyun
    • 한국데이터정보과학회:학술대회논문집
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    • pp.164-173
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    • 2005
  • 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. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, data reduction and variable screening, category merging, etc. We analyze waste database united with local information using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

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DESIGN OF A BINARY DECISION TREE FOR RECOGNITION OF THE DEFECT PATTERNS OF COLD MILL STRIP USING GENETIC ALGORITHM

  • Lee, Byung-Jin;Kyoung Lyou;Park, Gwi-Tae;Kim, Kyoung-Min
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.208-212
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    • 1998
  • This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.

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Comparison among Algorithms for Decision Tree based on Sasang Constitutional Clinical Data (사상체질 임상자료 기반 의사결정나무 생성 알고리즘 비교)

  • Jin, Hee-Jeong;Lee, Su-Kyung;Lee, Si-Woo
    • Korean Journal of Oriental Medicine
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    • v.17 no.2
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    • pp.121-127
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    • 2011
  • Objectives : In the clinical field, it is important to understand the factors that have effects on a certain disease or symptom. For this, many researchers apply Data Mining method to the clinical data that they have collected. One of the efficient methods for Data Mining is decision tree induction. Many researchers have studied to find the best split criteria of decision tree; however, various split criteria coexist. Methods : In this paper, we applied several split criteria(Information Gain, Gini Index, Chi-Square) to Sasang constitutional clinical information and compared each decision tree in order to find optimal split criteria. Results & Conclusion : We found BMI and body measurement factors are important factors to Sasang constitution by analyzing produced decision trees with different split measures. And the decision tree using information gain had the highest accuracy. However, the decision tree that produced highest accuracy is changed depending on given data. So, researcher have to try to find proper split criteria for given data by understanding attribute of the given data.

A Study on the Categorization of Context-dependent Phoneme using Decision Tree Modeling (결정 트리 모델링에 의한 한국어 문맥 종속 음소 분류 연구)

  • 이선정
    • Journal of the Korea Computer Industry Society
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    • v.2 no.2
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    • pp.195-202
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    • 2001
  • In this paper, we show a study on how to model a phoneme of which acoustic feature is changed according to both left-hand and right-hand phonemes. For this purpose, we make a comparative study on two kinds of algorithms; a unit reduction algorithm and decision tree modeling. The unit reduction algorithm uses only statistical information while the decision tree modeling uses statistical information and Korean acoustical information simultaneously. Especially, we focus on how to model context-dependent phonemes based on decision tree modeling. Finally, we show the recognition rate when context-dependent phonemes are obtained by the decision tree modeling.

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A Decision Tree Induction using Genetic Programming with Sequentially Selected Features (순차적으로 선택된 특성과 유전 프로그래밍을 이용한 결정나무)

  • Kim Hyo-Jung;Park Chong-Sun
    • Korean Management Science Review
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    • v.23 no.1
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    • pp.63-74
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    • 2006
  • Decision tree induction algorithm is one of the most widely used methods in classification problems. However, they could be trapped into a local minimum and have no reasonable means to escape from it if tree algorithm uses top-down search algorithm. Further, if irrelevant or redundant features are included in the data set, tree algorithms produces trees that are less accurate than those from the data set with only relevant features. We propose a hybrid algorithm to generate decision tree that uses genetic programming with sequentially selected features. Correlation-based Feature Selection (CFS) method is adopted to find relevant features which are fed to genetic programming sequentially to find optimal trees at each iteration. The new proposed algorithm produce simpler and more understandable decision trees as compared with other decision trees and it is also effective in producing similar or better trees with relatively smaller set of features in the view of cross-validation accuracy.

A Decision Tree Approach for Identifying Defective Products in the Manufacturing Process

  • Choi, Sungsu;Battulga, Lkhagvadorj;Nasridinov, Aziz;Yoo, Kwan-Hee
    • International Journal of Contents
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    • v.13 no.2
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    • pp.57-65
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    • 2017
  • Recently, due to the significance of Industry 4.0, the manufacturing industry is developing globally. Conventionally, the manufacturing industry generates a large volume of data that is often related to process, line and products. In this paper, we analyzed causes of defective products in the manufacturing process using the decision tree technique, that is a well-known technique used in data mining. We used data collected from the domestic manufacturing industry that includes Manufacturing Execution System (MES), Point of Production (POP), equipment data accumulated directly in equipment, in-process/external air-conditioning sensors and static electricity. We propose to implement a model using C4.5 decision tree algorithm. Specifically, the proposed decision tree model is modeled based on components of a specific part. We propose to identify the state of products, where the defect occurred and compare it with the generated decision tree model to determine the cause of the defect.

Selection of the Optimal Decision Tree Model Using Grid Search Method : Focusing on the Analysis of the Factors Affecting Job Satisfaction of Workplace Reserve Force Commanders (격자탐색법을 이용한 의사결정나무 분석 최적 모형 선택 : 직장예비군 지휘관의 직장만족도에 대한 영향 요인 분석을 중심으로)

  • Jeong, Chulwoo;Jeong, Won Young;Shin, David
    • Journal of the Korean Operations Research and Management Science Society
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    • v.40 no.2
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    • pp.19-29
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    • 2015
  • The purpose of this study is to suggest the grid search method for selecting an optimal decision tree model. It chooses optimal values for the maximum depth of tree and the minimum number of observations that must exist in a node in order for a split to be attempted. Therefore, the grid search method guarantees building a decision tree model that shows more precise and stable classifying performance. Through empirical analysis using data of job satisfaction of workplace reserve force commanders, we show that the grid search method helps us generate an optimal decision tree model that gives us hints for the improvement direction of labor conditions of Korean workplace reserve force commanders.

Fault Diagnosis of Induction Motors using Decision Trees (결정목을 이용한 유도전동기 결함진단)

  • Tran Van Tung;Yang Bo-Suk;Oh Myung-Suck
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • pp.407-410
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    • 2006
  • Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine teaming, pattern recognition, and data mining have considered the decision tree method as an effective solution to their field problems. In this paper, an application of decision tree method to classify the faults of induction motors is proposed. The original data from experiment is dealt with feature calculation to get the useful information as attributes. These data are then assigned the classes which are based on our experience before becoming data inputs for decision tree. The total 9 classes are defined. An implementation of decision tree written in Matlab is used for four data sets with good performance results

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Multivariate Decision Tree for High -dimensional Response Vector with Its Application

  • Lee, Seong-Keon
    • Communications for Statistical Applications and Methods
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    • v.11 no.3
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    • pp.539-551
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    • 2004
  • Multiple responses are often observed in many application fields, such as customer's time-of-day pattern for using internet. Some decision trees for multiple responses have been constructed by many researchers. However, if the response is a high-dimensional vector that can be thought of as a discretized function, then fitting a multivariate decision tree may be unsuccessful. Yu and Lambert (1999) suggested spline tree and principal component tree to analyze high dimensional response vector by using dimension reduction techniques. In this paper, we shall propose factor tree which would be more interpretable and competitive. Furthermore, using Korean internet company data, we will analyze time-of-day patterns for internet user.

Optimization of Decision Tree for Classification Using a Particle Swarm

  • Cho, Yun-Ju;Lee, Hye-Seon;Jun, Chi-Hyuck
    • Industrial Engineering and Management Systems
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    • v.10 no.4
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    • pp.272-278
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    • 2011
  • Decision tree as a classification tool is being used successfully in many areas such as medical diagnosis, customer churn prediction, signal detection and so on. The main advantage of decision tree classifiers is their capability to break down a complex structure into a collection of simpler structures, thus providing a solution that is easy to interpret. Since decision tree is a top-down algorithm using a divide and conquer induction process, there is a risk of reaching a local optimal solution. This paper proposes a procedure of optimally determining thresholds of the chosen variables for a decision tree using an adaptive particle swarm optimization (APSO). The proposed algorithm consists of two phases. First, we construct a decision tree and choose the relevant variables. Second, we find the optimum thresholds simultaneously using an APSO for those selected variables. To validate the proposed algorithm, several artificial and real datasets are used. We compare our results with the original CART results and show that the proposed algorithm is promising for improving prediction accuracy.