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Assessing the Predictive Utility of Logistic regression, classification and regression tree, Chi-Squared Automatic Interaction Detection, and Neural Network Models in Predicting Inmate Misconduct

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AMERICAN JOURNAL OF CRIMINAL JUSTICE 2015年第1期40卷 47-74页

作者： Ngo, Fawn Govindu, Ramakrishna Agarwal, Anurag Univ South Florida Sarasota Manatee Coll Arts & Sci 8350 N Tmiami Trail Sarasota FL 34243 USA Univ South Florida Sarasota Manatee Coll Business Sarasota FL 34243 USA

This study assesses the relative utility of a traditional regression approach logistic regression (LR) - and three classification techniques - classification and regression tree (CART), chi-squared automatic interaction detection (CHAID), and multi-layer perceptron neural network (MLPNN)-in predicting inmate misconduct. The four models were tested using a sample of inmates held in state and federal prisons and predictors derived from the importation model on inmate adaptation. Multivalidation procedure and multiple evaluation indicators were used to evaluate and report the predictive accuracy. The overall accuracy of the four models varied between 0.60 and 0.66 with an overall AUC range of 0.60-0.70. The LR and MLPNN methods performed significantly better than the CART and CHAID techniques at identifying misbehaving inmates and the CHAID method outperformed the CART approach in classifying defied inmates. The MLPNN method performed significantly better than the LR technique in predicting inmate misconduct among the training samples.

关键词： Actuarial risk assessment techniques Comparative statistical techniques Logistic regression classification and regression tree Chi-squared automatic interaction detection Neural networks Importation model Inmate misconduct

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Incident Duration Model on Urban Freeways Based on classification and regression tree

Incident Duration Model on Urban Freeways Based on Classific...

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2nd International Conference on Intelligent Computation Technology and Automation

作者： Zhao Xiaoqiang Li Ruimin Yu Xinxin Tsinghua Univ Inst Transportat Engn Beijing 100084 Peoples R China

ISBN: (纸本)9780769538044

Effective incident management requires accurate prediction of incident duration. In this paper, classification and regression tree (CART) is employed to model the incident duration. All 65000 incident records from Beijing Transportation Management Bureau are used for model establishment and another 8000 records for validation. The average relative error of the CART model is 29.5197%. It shows that the reliability of the model is quite satisfactory. The average relative error of the prediction on different ring roads of Beijing is approximately the same.

关键词： Decision tree classification and regression tree Incident duration Mutiple linear regression

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Predicting STEM Major Choice: a Machine Learning classification and regression tree Approach

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Journal for STEM Education Research 2023年第2期6卷 358-374页

作者： Chang, Chi-Ning Lin, Shuqiong Kwok, Oi-Man Saw, Guan Kung Virginia Commonwealth University Richmond VA United States American Institutes for Research Austin TX United States Texas A&M University College Station TX United States Claremont Graduate University Claremont CA United States

Despite the increasing demand for professionals in science, technology, engineering, and mathematics (STEM), only a small portion of young people in the USA pursue a postsecondary degree in STEM. To identify the major predictors of STEM participation, this study uses a machine learning approach, a classification and regression tree (CART), to analyze a wide range of individual, family, and school factors obtained from national survey data of US high school freshmen in fall 2009 who eventually enrolled in STEM college majors by 2016. The analytic results indicate that calculus credits, science identity, total STEM credits, and math achievement are the most predictive factors during the high school years of college STEM major selection. The CART-based tree also shows how these four variables interactively predict the likelihood of students enrolling in STEM college majors. © 2023, The Author(s).

关键词： classification and regression tree Course-taking High School Longitudinal Study of 2009–2016 Motivation STEM college major

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A Comparison of Logistic regression, classification and regression tree, and Neural Networks Models in Predicting Violent Re-Offending

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JOURNAL OF QUANTITATIVE CRIMINOLOGY 2011年第4期27卷 547-573页

作者： Liu, Yuan Y. Yang, Min Ramsay, Malcolm Li, Xiao S. Coid, Jeremy W. Sichuan Univ Sch Publ Hlth Dept Hlth Stat Chengdu 610041 Peoples R China Univ Nottingham Sch Community Hlth Sci Div Psychiat Nottingham NG7 2TU England Minist Justice Partnerships & Hlth Strategy Unit London SW1H 9AJ England Queen Mary Univ London Forens Psychiat Res Unit London EC1A 7BE England

Previous studies that have compared logistic regression (LR), classification and regression tree (CART), and neural networks (NNs) models for their predictive validity have shown inconsistent results in demonstrating superiority of any one model. The three models were tested in a prospective sample of 1225 UK male prisoners followed up for a mean of 3.31 years after release. Items in a widely-used risk assessment instrument (the Historical, Clinical, Risk Management-20, or HCR-20) were used as predictors and violent reconvictions as outcome. Multi-validation procedure was used to reduce sampling error in reporting the predictive accuracy. The low base rate was controlled by using different measures in the three models to minimize prediction error and achieve a more balanced classification. Overall accuracy of the three models varied between 0.59 and 0.67, with an overall AUC range of 0.65-0.72. Although the performance of NNs was slightly better than that of LR and CART models, it did not demonstrate a significant improvement.

关键词： Violence reconviction Risk assessment Neural networks classification and regression tree HCR-20

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Multistage MR-CART: Multiresponse optimization in a multistage process using a classification and regression tree method

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 159卷

作者： Lee, Dong-Hee Kim, So-Hee Kim, Kwang-Jae Hanyang Univ Div Ind Informat Studies 222 Wangsimni Ro Seoul 04763 South Korea Pohang Univ Sci & Technol Dept Ind & Management Engn 77 Cheongam Ro Pohang 37673 Gyeongbuk South Korea

A multistage process consists of sequential consecutive stages. In this process, each stage has multiple responses and is affected by its preceding stage, while at the same time, affecting the following stage. This complex structure makes it difficult to optimize the multistage process. Recently, it became easy to obtain a large amount of operational data from the multistage process due to development of information technologies. The proposed method employs a data mining method called a classification and regression tree for analyzing the data and desirability functions for simultaneously optimizing the multiresponse. To consider the relationship between stages, a backward optimization procedure which treats the multiresponse of the preceding stage as the input variables is proposed. The proposed method is described using a steel manufacturing process example and is compared with existing multiresponse optimization methods. The case study shows that the proposed method works well and outperforms the existing methods.

关键词： Multistage process Multiresponse optimization Desirability function Big data Data mining classification and regression tree

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A Fault Isolation Method via classification and regression tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant

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ENERGIES 2018年第5期11卷

作者： Yu, Jungwon Jang, Jaeyel Yoo, Jaeyeong Park, June Ho Kim, Sungshin Busan Natl Univ Dept Elect & Comp Engn Busan 46241 South Korea Korea East West Power Co Ltd Tech Solut Ctr Technol & Informat Dept Dangjin 31700 South Korea XEONET Co Ltd CTO Seongnam 13216 South Korea

Accurate detection and isolation of possible faults are indispensable for operating complex industrial processes more safely, effectively, and economically. In this paper, we propose a fault isolation method for steam boilers in thermal power plants via classification and regression tree (CART)-based variable ranking. In the proposed method, binary classification trees are constructed by applying the CART algorithm to a training dataset which is composed of normal and faulty samples for classifier learning then, to perform faulty variable isolation, variable importance values for each input variable are extracted from the constructed trees. The importance values for non-faulty variables are not influenced by faulty variables, because the values are extracted from the trees with decision boundaries only in the original input space;the proposed method does not suffer from smearing effect. Furthermore, the proposed method, based on the nonparametric CART classifier, can be applicable to nonlinear processes. To confirm the effectiveness, the proposed and comparison methods are applied to two benchmark problems and 250 MW drum-type steam boiler. Experimental results show that the proposed method isolates faulty variables more clearly without the smearing effect than the comparison methods.

关键词： drum-type steam boiler fault isolation classification and regression tree variable ranking smearing effect

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NCART: Neural classification and regression tree for tabular data

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PATTERN RECOGNITION 2024年 154卷

作者： Luo, Jiaqi Xu, Shixin Duke Kunshan Univ Data Sci Res Ctr 8 Duke Ave Kunshan 215000 Jiangsu Peoples R China

Deep learning models have become popular in the analysis of tabular data, as they address the limitations of decision trees and enable valuable applications like semi -supervised learning, online learning, and transfer learning. However, these deep -learning approaches often encounter a trade-off. On one hand, they can be computationally demanding when dealing with large-scale or high -dimensional datasets. On the other hand, they may lack interpretability and may not be suitable for small-scale datasets. In this study, we propose a novel interpretable neural network called Neural classification and regression tree (NCART) to overcome these challenges. NCART is a modified version of Residual Networks that replaces fully -connected layers with multiple differentiable oblivious decision trees. By integrating decision trees into the architecture, NCART maintains its interpretability while benefiting from the end -to -end capabilities of neural networks. The simplicity of the NCART architecture makes it well -suited for datasets of varying sizes and reduces computational costs compared to state-of-the-art deep learning models. Extensive numerical experiments demonstrate the superior performance of NCART compared to existing deep learning models, establishing it as a strong competitor to tree -based models. The code is available at https://***/Luojiaqimath/NCART.

关键词： Tabular data Neural networks Interpretability classification and regression tree

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An investigation of the hydrological requirements of River Red Gum (Eucalyptus camaldulensis) Forest, using classification and regression tree modelling

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ECOHYDROLOGY 2009年第2期2卷 143-155页

作者： Wen, Li Ling, Joanne Saintilan, Neil Rogers, Kerrylee NSW Dept Environm & Climate Change Rivers & Wetlands Unit Sci Serv Div Sydney NSW 2000 Australia

River Red Gum (Eucalyptus camaldulensis) is widely distributed throughout many water Courses and floodplains within inland Australia. Ill recent years. accelerated decline of River Red GUM condition has been observed ill many locations. and field observations of the degradation are consistent with the reduction of flooding. However, there are few publications that quantitatively investigate the relationships between River Red Gum condition and flooding history. We applied classification and regression tree (CART) to model the minimum flooding requirement of River Red Gum forest/woodland ill Yanga National Park, located oil the Lower Murrumbidgee Floodplain, Southeast Australia. using crown conditions derived from historical aerial photographs spanning more than 40 years. The Model produced has a moderate reliability with all overall accuracy of 64.1% and a Kappa index of 0.543. The model brings ill important insights, about the relationship between River Red GUM community type, flood frequency and flood duration. Our results demonstrated that ( I) CART analysis is a simple yet powerful technique with significant potential for application ill river mid environmental flow management (2) River Red Gum communities on the Lower Murrumbidgee Floodplain require periodic inundation (3-5 years) for a duration of up to 64 days to be ill moderate to good conditions (3) Although the crown conditions, Of different community types displayed similar degradation trends. they have distinct flooding requirements: and (4) The River Red Gum community ill Yanga National Park may be managed is hydrological units given limited environmental water allocations. Copyright (C) 2009 John Wiley & Sons, Ltd.

关键词： classification and regression tree River Red Gum crown condition flooding history floodplain environmental water

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Diagnosis of diabetes mellitus using artificial neural network and classification and regression tree optimized with genetic algorithm

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JOURNAL OF FORECASTING 2020年第4期39卷 661-670页

作者： Pekel Ozmen, Ebru Ozcan, Tuncay Istanbul Univ Cerrahpasa Dept Ind Engn Istanbul Turkey Istanbul Tech Univ Management Engn Dept Istanbul Turkey

Diabetes mellitus is one of the most important public health problems affecting millions of people worldwide. An early and accurate diagnosis of diabetes mellitus has critical importance for the medical treatments of patients. In this study, first, artificial neural network (ANN) and classification and regression tree (CART)-based approaches are proposed for the diagnosis of diabetes. Hybrid ANN-GA and CART-GA approaches are then developed using a genetic algorithm (GA) to improve the classification accuracy of these approaches. Finally, the performances of the developed approaches are evaluated with a Pima Indian diabetes data set. Experimental results show that the developed hybrid CART-GA approach outperforms the ANN, CART, and ANN-GA approaches in terms of classification accuracy, and this approach provides an efficient methodology for diagnosis of diabetes mellitus.

关键词： artificial neural network classification classification and regression tree diabetes genetic algorithm

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Applying classification and regression tree analysis to identify risks of developing sarcopenia in the older population

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INTERNATIONAL JOURNAL OF OLDER PEOPLE NURSING 2022年第6期17卷 e12488页

作者： Nga Thi Thuy Nguyen Huyen Thi Thanh Vu Huey Lan Hu Lin, Kuan Chia Thanh Xuan Nguyen Huang, Hui Chuan Taipei Med Univ Sch Nursing Taipei Taiwan Hanoi Med Univ Fac Nursing & Midwifery Hanoi Vietnam Hanoi Med Univ Dept Geriatr & Gerontol Hanoi Vietnam Natl Geriatr Hosp Sci Res Dept Hanoi Vietnam Natl Yang Ming Chiao Tung Univ Coll Nursing Sch Nursing Taipei Taiwan Natl Yang Ming Chiao Tung Univ Inst Hosp & Hlth Care Management Community Res Ctr Taipei Taiwan Cheng Hsin Gen Hosp Taipei Taiwan

Background and objective: Variations in the risk factors for sarcopenia can lead to differences in the likelihood of developing sarcopenia among older adults;however, few studies have explored the interactions among the risk factors. This study examined the interactions among risk factors and identified a discriminative pathway for groups at risk of sarcopenia in community-dwelling older adults. Methods: A cross-sectional study was conducted between July and August 2019 to recruit 200 older adults from an outpatient department of a hospital providing care for older people. Data on various risk factors, namely demographics (age, gender, education, comorbidities, and body mass index [BMI]), dietary habits (weekly consumption of milk, coffee, and meat), lifestyle behaviours (vitamin D supplementation, smoking, drinking, and physical activity), and depression symptoms were collected. Sarcopenia was defined according to the Asian Working Group for Sarcopenia criteria. A classification and regression tree (CART) model was used to examine interactions among these factors and identify groups at risk of sarcopenia. Findings: The prevalence of sarcopenia was 38.5%. The CART model identified two end groups at differential risks of sarcopenia, with a minimum of one and a maximum of three risk factors. In the first group, low BMI (<18.5 kg/m(2)) was a predominant risk factor for sarcopenia among older people. In the second group, older adults with a normal BMI, aged >= 68 years, and without a regular walking habit had a higher probability of developing sarcopenia than did their counterparts. Conclusions: The interactive effects among older age, BMI, and walking may cause different probabilities of developing sarcopenia in the older population. Implications for practice: Older adults with a low or normal BMI but without a regular walking habit could be a predominant risk group for sarcopenia. The appropriate maintenance of body weight and regular walking activity is suggested to

关键词： body mass index classification and regression tree older age risk factor sarcopenia walking

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