• Title, Summary, Keyword: Decision Tree

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Ensemble of Fuzzy Decision Tree for Efficient Indoor Space Recognition

  • Kim, Kisang;Choi, Hyung-Il
    • Journal of the Korea Society of Computer and Information
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    • v.22 no.4
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    • pp.33-39
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    • 2017
  • In this paper, we expand the process of classification to an ensemble of fuzzy decision tree. For indoor space recognition, many research use Boosted Tree, consists of Adaboost and decision tree. The Boosted Tree extracts an optimal decision tree in stages. On each stage, Boosted Tree extracts the good decision tree by minimizing the weighted error of classification. This decision tree performs a hard decision. In most case, hard decision offer some error when they classify nearby a dividing point. Therefore, We suggest an ensemble of fuzzy decision tree, which offer some flexibility to the Boosted Tree algorithm as well as a high performance. In experimental results, we evaluate that the accuracy of suggested methods improved about 13% than the traditional one.

Decision Tree with Optimal Feature Selection for Bearing Fault Detection

  • Nguyen, Ngoc-Tu;Lee, Hong-Hee
    • Journal of Power Electronics
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    • v.8 no.1
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    • pp.101-107
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    • 2008
  • In this paper, the features extracted from vibration time signals are used to detect the bearing fault condition. The decision tree is applied to diagnose the bearing status, which has the benefits of being an expert system that is based on knowledge history and is simple to understand. This paper also suggests a genetic algorithm (GA) as a method to reduce the number of features. In order to show the potentials of this method in both aspects of accuracy and simplicity, the reduced-feature decision tree is compared with the non reduced-feature decision tree and the PCA-based decision tree.

Unseen Model Prediction using an Optimal Decision Tree (Optimal Decision Tree를 이용한 Unseen Model 추정방법)

  • Kim Sungtak;Kim Hoi-Rin
    • MALSORI
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    • no.45
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    • pp.117-126
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    • 2003
  • Decision tree-based state tying has been proposed in recent years as the most popular approach for clustering the states of context-dependent hidden Markov model-based speech recognition. The aims of state tying is to reduce the number of free parameters and predict state probability distributions of unseen models. But, when doing state tying, the size of a decision tree is very important for word independent recognition. In this paper, we try to construct optimized decision tree based on the average of feature vectors in state pool and the number of seen modes. We observed that the proposed optimal decision tree is effective in predicting the state probability distribution of unseen models.

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A Comparative Study of Medical Data Classification Methods Based on Decision Tree and System Reconstruction Analysis

  • Tang, Tzung-I;Zheng, Gang;Huang, Yalou;Shu, Guangfu;Wang, Pengtao
    • Industrial Engineering and Management Systems
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    • v.4 no.1
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    • pp.102-108
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    • 2005
  • This paper studies medical data classification methods, comparing decision tree and system reconstruction analysis as applied to heart disease medical data mining. The data we study is collected from patients with coronary heart disease. It has 1,723 records of 71 attributes each. We use the system-reconstruction method to weight it. We use decision tree algorithms, such as induction of decision trees (ID3), classification and regression tree (C4.5), classification and regression tree (CART), Chi-square automatic interaction detector (CHAID), and exhausted CHAID. We use the results to compare the correction rate, leaf number, and tree depth of different decision-tree algorithms. According to the experiments, we know that weighted data can improve the correction rate of coronary heart disease data but has little effect on the tree depth and leaf number.

Improvement of a Decision Tree for The Rehabilitation of Asphalt Pavement in City Road (도심지 아스팔트 포장의 유지보수공법 의사결정 절차 개선)

  • Park, Chang Kyu;Kim, Won Jae;Kim, Tae Woo;Lee, Jin Wook;Baek, Jong Eun;Lee, Hyun Jong
    • International Journal of Highway Engineering
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    • v.20 no.3
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    • pp.27-37
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    • 2018
  • PURPOSES : The objective of this study is to develop a pavement rehabilitation decision tree considering current pavement condition by evaluating severity and distress types such as roughness, cracking and rutting. METHODS : To improve the proposed overall rehabilitation decision tree, current decision tree from Korea and decision trees from other countries were summarized and investigated. The problem when applying the current rehabilitation method obtained from the decision tree applied in Seoul was further analyzed. It was found that the current decision trees do not consider different distress characteristics such as crack type, road types and functions. Because of this, different distress values for IRI, crack rate and plastic deformation was added to the proposed decision tree to properly recommend appropriate pavement rehabilitation. Utilizing the 2017 Seoul pavement management system data and considering all factors as discussed, the proposed overall decision tree was revised and improved. RESULTS :In this study, the type of crack was included to the decision tree. Meanwhile current design thickness and special asphalt mixture were studied and improved to be applied on different pavement condition. In addition, the improved decision tree was incorporated with the Seoul asphalt overlay design program. In the case of Seoul's rehabilitation budget, rehabilitation budget can be optimized if a 25mm milling and overlay thickness is used. CONCLUSIONS:A practical and theoretical evaluation tool in pavement rehabilitation design was presented and proposed for Seoul City.

Two-Stage Decision Tree Analysis for Diagnosis of Personal Sasang Constitution Medicine Type (사상체질 판별을 위한 2단계 의사결정 나무 분석)

  • Jin, Hee-Jeong;Lee, Hae-Jung;Kim, Myoung-Geun;Kim, Hong-Gie;Kim, Jong-Yeol
    • Journal of Sasang Constitutional Medicine
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    • v.22 no.3
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    • pp.87-97
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    • 2010
  • 1. Objectives: In SCM, a personal Sasang constitution must be determined accurately before any Sasang treatment. The purpose of this study is to develop an objective method for classification of Sasang constitution. 2. Methods: We collected samples from 5 centers where SCM is practiced, and applied two-stage decision tree analysis on these samples. We recruited samples from 5 centers. The collected data were from subjects whose response to herbal medicine was confirmed according to Sasang constitution. 3. Results: The two-stage decision tree model shows higher classification power than a simple decision tree model. This study also suggests that gender must be considered in the first stage to improve the accuracy of classification. 4. Conclusions: We identified important factors for classifying Sasang constitutions through two-stage decision tree analysis. The two-stage decision tree model shows higher classification power than a simple decision tree model.

Learning Algorithm for Multiple Distribution Data using Haar-like Feature and Decision Tree (다중 분포 학습 모델을 위한 Haar-like Feature와 Decision Tree를 이용한 학습 알고리즘)

  • Kwak, Ju-Hyun;Woen, Il-Young;Lee, Chang-Hoon
    • KIPS Transactions on Software and Data Engineering
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    • v.2 no.1
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    • pp.43-48
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    • 2013
  • Adaboost is widely used for Haar-like feature boosting algorithm in Face Detection. It shows very effective performance on single distribution model. But when detecting front and side face images at same time, Adaboost shows it's limitation on multiple distribution data because it uses linear combination of basic classifier. This paper suggest the HDCT, modified decision tree algorithm for Haar-like features. We still tested the performance of HDCT compared with Adaboost on multiple distributed image recognition.

Modeling of Environmental Survey by Decision Trees

  • Park, Hee-Chang;Cho, Kwang-Hyun
    • 한국데이터정보과학회:학술대회논문집
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    • pp.63-75
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    • 2004
  • 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 dection, data reduction and variable screening, category merging, etc. We analyze Gyeongnam social indicator survey data using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

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Modeling of Environmental Survey by Decision Trees

  • Park, Hee-Chang;Cho, Kwang-Hyun
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.4
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    • pp.759-771
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    • 2004
  • 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 dection, data reduction and variable screening, category merging, etc. We analyze Gyeongnam social indicator survey data using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

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An Application of Decision Tree Method for Fault Diagnosis of Induction Motors

  • Tran, Van Tung;Yang, Bo-Suk;Oh, Myung-Suck
    • Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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    • pp.54-59
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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 learning, 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 these data.

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