• Title/Summary/Keyword: Classification and Regression Tree

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Note on classification and regression tree analysis (분류와 회귀나무분석에 관한 소고)

  • 임용빈;오만숙
    • Journal of Korean Society for Quality Management
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    • 제30권1호
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    • pp.152-161
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    • 2002
  • The analysis of large data sets with hundreds of thousands observations and thousands of independent variables is a formidable computational task. A less parametric method, capable of identifying important independent variables and their interactions, is a tree structured approach to regression and classification. It gives a graphical and often illuminating way of looking at data in classification and regression problems. In this paper, we have reviewed and summarized tile methodology used to construct a tree, multiple trees and the sequential strategy for identifying active compounds in large chemical databases.

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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    • 제4권1호
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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.

A review of tree-based Bayesian methods

  • Linero, Antonio R.
    • Communications for Statistical Applications and Methods
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    • 제24권6호
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    • pp.543-559
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    • 2017
  • Tree-based regression and classification ensembles form a standard part of the data-science toolkit. Many commonly used methods take an algorithmic view, proposing greedy methods for constructing decision trees; examples include the classification and regression trees algorithm, boosted decision trees, and random forests. Recent history has seen a surge of interest in Bayesian techniques for constructing decision tree ensembles, with these methods frequently outperforming their algorithmic counterparts. The goal of this article is to survey the landscape surrounding Bayesian decision tree methods, and to discuss recent modeling and computational developments. We provide connections between Bayesian tree-based methods and existing machine learning techniques, and outline several recent theoretical developments establishing frequentist consistency and rates of convergence for the posterior distribution. The methodology we present is applicable for a wide variety of statistical tasks including regression, classification, modeling of count data, and many others. We illustrate the methodology on both simulated and real datasets.

Integrity Assessment for Reinforced Concrete Structures Using Fuzzy Decision Making (퍼지의사결정을 이용한 RC구조물의 건전성평가)

  • 박철수;손용우;이증빈
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 한국전산구조공학회 2002년도 봄 학술발표회 논문집
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    • pp.274-283
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    • 2002
  • This paper presents an efficient models for reinforeced concrete structures using CART-ANFIS(classification and regression tree-adaptive neuro fuzzy inference system). a fuzzy decision tree parttitions the input space of a data set into mutually exclusive regions, each of which is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the Predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it everywhere continuous and smooth. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

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Selecting Machine Learning Model Based on Natural Language Processing for Shanghanlun Diagnostic System Classification (자연어 처리 기반 『상한론(傷寒論)』 변병진단체계(辨病診斷體系) 분류를 위한 기계학습 모델 선정)

  • Young-Nam Kim
    • 대한상한금궤의학회지
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    • 제14권1호
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    • pp.41-50
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    • 2022
  • Objective : The purpose of this study is to explore the most suitable machine learning model algorithm for Shanghanlun diagnostic system classification using natural language processing (NLP). Methods : A total of 201 data items were collected from 『Shanghanlun』 and 『Clinical Shanghanlun』, 'Taeyangbyeong-gyeolhyung' and 'Eumyangyeokchahunobokbyeong' were excluded to prevent oversampling or undersampling. Data were pretreated using a twitter Korean tokenizer and trained by logistic regression, ridge regression, lasso regression, naive bayes classifier, decision tree, and random forest algorithms. The accuracy of the models were compared. Results : As a result of machine learning, ridge regression and naive Bayes classifier showed an accuracy of 0.843, logistic regression and random forest showed an accuracy of 0.804, and decision tree showed an accuracy of 0.745, while lasso regression showed an accuracy of 0.608. Conclusions : Ridge regression and naive Bayes classifier are suitable NLP machine learning models for the Shanghanlun diagnostic system classification.

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The Prediction Performance of the CART Using Bank and Insurance Company Data (CART의 예측 성능:은행 및 보험 회사 데이터 사용)

  • Park, Jeong-Seon
    • The Transactions of the Korea Information Processing Society
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    • 제3권6호
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    • pp.1468-1472
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    • 1996
  • In this study, the performance of the CART(Classification and Regression Tree) is compared with that of discriminant analysis method. In most experiments using bank data, discriminant analysis shows better performance in terms of the total cost. In contrast, most experiments using insurance data show that the CART is better than discriminant analysis in terms of the total cost. The contradictory result are analysed by using the characteristics of the data sets. The performances of both the Classification and Regression Tree and discriminant analysis depend on the parameters:failure prior probability, data used, type I error, type II error cost, and validation method.

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Selection of Important Variables in the Classification Model for Successful Flight Training (조종사 비행훈련 성패예측모형 구축을 위한 중요변수 선정)

  • Lee, Sang-Heon;Lee, Sun-Doo
    • IE interfaces
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    • 제20권1호
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    • pp.41-48
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    • 2007
  • The main purpose of this paper is cost reduction in absurd pilot positive expense and human accident prevention which is caused by in the pilot selection process. We use classification models such as logistic regression, decision tree, and neural network based on aptitude test results of 505 ROK Air Force applicants in 2001~2004. First, we determine the reliability and propriety against the aptitude test system which has been improved. Based on this conference flight simulator test item was compared to the new aptitude test item in order to make additional yes or no decision from different models in terms of classification accuracy, ROC and Response Threshold side. Decision tree was selected as the most efficient for each sequential flight training result and the last flight training results predict excellent. Therefore, we propose that the standard of pilot selection be adopted by the decision tree and it presents in the aptitude test item which is new a conference flight simulator test.

Enhancement of Text Classification Method (텍스트 분류 기법의 발전)

  • Shin, Kwang-Seong;Shin, Seong-Yoon
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 한국정보통신학회 2019년도 춘계학술대회
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    • pp.155-156
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    • 2019
  • Traditional machine learning based emotion analysis methods such as Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) are less accurate. In this paper, we propose an improved kNN classification method. Improved methods and data normalization achieve the goal of improving accuracy. Then, three classification algorithms and an improved algorithm were compared based on experimental data.

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Comparison of Classification Models for Sequential Flight Test Results (단계별 비행훈련 성패 예측 모형의 성능 비교 연구)

  • Sohn, So-Young;Cho, Yong-Kwan;Choi, Sung-Ok;Kim, Young-Joun
    • Journal of the Ergonomics Society of Korea
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    • 제21권1호
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    • pp.1-14
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    • 2002
  • The main purpose of this paper is to present selection criteria for ROK Airforce pilot training candidates in order to save costs involved in sequential pilot training. We use classification models such Decision Tree, Logistic Regression and Neural Network based on aptitude test results of 288 ROK Air Force applicants in 1994-1996. Different models are compared in terms of classification accuracy, ROC and Lift-value. Neural network is evaluated as the best model for each sequential flight test result while Logistic regression model outperforms the rest of them for discriminating the last flight test result. Therefore we suggest a pilot selection criterion based on this logistic regression. Overall. we find that the factors such as Attention Sharing, Speed Tracking, Machine Comprehension and Instrument Reading Ability having significant effects on the flight results. We expect that the use of our criteria can increase the effectiveness of flight resources.

Comparison of Data Mining Classification Algorithms for Categorical Feature Variables (범주형 자료에 대한 데이터 마이닝 분류기법 성능 비교)

  • Sohn, So-Young;Shin, Hyung-Won
    • IE interfaces
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    • 제12권4호
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    • pp.551-556
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    • 1999
  • In this paper, we compare the performance of three data mining classification algorithms(neural network, decision tree, logistic regression) in consideration of various characteristics of categorical input and output data. $2^{4-1}$. 3 fractional factorial design is used to simulate the comparison situation where factors used are (1) the categorical ratio of input variables, (2) the complexity of functional relationship between the output and input variables, (3) the size of randomness in the relationship, (4) the categorical ratio of an output variable, and (5) the classification algorithm. Experimental study results indicate the following: decision tree performs better than the others when the relationship between output and input variables is simple while logistic regression is better when the other way is around; and neural network appears a better choice than the others when the randomness in the relationship is relatively large. We also use Taguchi design to improve the practicality of our study results by letting the relationship between the output and input variables as a noise factor. As a result, the classification accuracy of neural network and decision tree turns out to be higher than that of logistic regression, when the categorical proportion of the output variable is even.

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