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作者： Lin, Shuqiong Texas A&M University

学位级别：Ph.D., Doctor of Philosophy

The logistic regression model is the most commonly used analysis method for modeling binary data. Unbiased estimation using logistic regressions heavily depends on strong model assumptions which are often violated in reality. The classification and regression tree (CART) algorithm gains its popularity to replace the logistic regression, because CART does not require model assumptions and can model complex relationships automatically. However, only limited studies developed multilevel CART (M-CART) algorithms for modeling multilevel data with binary outcomes. Therefore, in the first study, a new M-CART algorithm was proposed for modeling multilevel data with binary outcomes which combines the multilevel logistic regression (M-logit) and the single-level CART (S-CART) using an expectation-maximization algorithm. This proposed algorithm allows inclusion of covariates at all levels, depends on no model assumptions, and captures interaction and nonlinearity in an automatic way. The performance of the proposed M-CART was compared with M-CART, S-CART, and single-level logistic regression (S-logit) in terms of prediction accuracy. Results from simulation study showed that M-CART lead to higher classification accuracy, sensitivity, specificity and Klecka's tau values than all other three methods. In the second study, the proposed M-CART algorithm was applied in propensity score analysis (PSA) when having multi-site non-randomized control trials (non-RCTs). PSA is the most popular statistical technique that estimates the casual effect of a treatment by eliminating the systematic differences of pre-treatment covariates between individuals who receive treatment and individuals who do not receive treatment. M-logit and S-CART have been applied to estimate propensity scores, while no study has explored the performance of using M-CART for estimation. Thus, in the second study, the performance of the proposed M-CART was compared with M-logit, S-CART, and S-logit in terms of covaria

关键词： Multilevel CART algorithms Propensity Score Analysis classification and regression tree

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ON treeD GAUSSIAN PROCESSES FOR MODELLING STRUCTURAL DYNAMIC SYSTEMS

ON TREED GAUSSIAN PROCESSES FOR MODELLING STRUCTURAL DYNAMI...

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作者： Zhang, Tianwei The University of Sheffield

学位级别：博士

The aim of this PhD project is to develop an approach to particle damper modelling based on Gaussian process regression trees (GPRTs). Developing validated particle damper models is a prerequisite for being able to optimise their performance when applied, for example, in the damping of various components and assemblies for a range of frequencies. The objective of the model is to make a credible prediction of damper behaviour for a given set of design parameters and input excitations. Particle damper modelling may be approached in one of three ways: analytically from first principles; numerically using finite or discrete element methods; or ex- perimentally, with the structure and parameters of a reduced-order model inferred from measured data. This project focuses on the third of these approaches, with low-order, physics-based models being combined with experimental training data. The aim is to both identify the model structure that most accurately captures the behaviour of the damper and to infer the model parameters. The model can then be used to predict damper behaviour for design parameters and excitation conditions that have not been tested experimentally. This is made more challenging by the fact that particle dampers will in general, display nonlinear dynamic behaviour which can lead, for example, to switching behaviour in the observed responses. Gaussian process (GP) regression has emerged as a powerful and adaptable approach for modelling experimental data. The basic GP is, however, best suited to modelling outputs that vary smoothly as a function of input values - something that is em- phatically not the case for switching behaviour. An alternative is to develop a treed model which partitions the input space into regions that display smooth behaviour and to infer a separate GP model within each partition. This project will focus on the application of these approaches to particle damper modelling, including inves- tigation of the contributing statistical te

关键词： Machine Learning Structural Dynamics Structural Health Monitoring Gaussian Processes Decision tree classification and regression tree Particle Damping

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Research on Intrusion Detection Based on Highly Interpretable Machine Learning 2

Research on Intrusion Detection Based on Highly Interpretabl...

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2nd International Conference on Intelligent Communication and Networking, ICN 2024

作者： Tian, Zhong Pan, Chenglong Li, Yiqiao Yang, Faxue Li, Zhongyue Zhongqi Zhilian Technology Co. Ltd Tianjin China

ISBN: (纸本)9798331519162

To address the issue of poor interpretability in existing intrusion detection algorithms, this paper proposes an automotive network intrusion detection algorithm based on classification and regression trees (CART). First, the dataset is discretized using binary classification points. Then, a highly interpretable CART model is constructed based on the Gini index as the criterion. Finally, the model is tested and validated. The experimental results show that the proposed method achieves good classification performance and provides a clear and interpretable decision-making process. © 2024 IEEE.

关键词： classification and regression tree Connected In-Vehicle Network Intelligent Intrusion Detection

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Promotion factors for enlisted infantry Marines

Promotion factors for enlisted infantry Marines

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作者： Micah A. Steinpfad Naval Postgraduate School

学位级别：硕士

The Marine Corps dedicates itself to ensuring quality retention and promotion. To accomplish this, we must analyze the effects of policy and the quality of Marines currently serving. This thesis considers data from 97,013 enlisted infantry Marines who entered the Marine Corps from 2001 to 2016 to determine which factors contribute toward promotion for the ranks of lance corporal, corporal, sergeant and staff sergeant. To assess which ranks proportionally promote more high-quality Marines, we compare two performance evaluation methods: proficiency and conduct marks and reporting senior and reviewing officer values. Our analysis of the data shows that the most important factors for promotion are conduct scores, reporting senior relative values, the number of deployments, combat fitness test (CFT) and physical fitness test (PFT) scores and zero adverse fitness reports. From the two performance evaluation methods we find that the Marine Corps promotes proportionally more high-quality Marines as reflected in fitness reports than with proficiency and conduct marks. Promoting and retaining the highest-performing Marines will ensure they are fully prepared to meet current and future challenges to national security. We must develop new methods for retaining more top-quality Marines to ensure their greatest probability of success.

关键词： Marine Corps enlisted promotion infantry predictors performance fitness reports rpart trees classification and regression tree proportion probability quality proficiency conduct reporting senior reviewing officer

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Decision tree -based diagnosis of coronary artery disease: CART model

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020年 192卷 105400-105400页

作者： Ghiasi, Mohammad M. Zendehboudi, Sohrab Mohsenipour, Ali Asghar Mem Univ Fac Engn & Appl Sci St John NF A1B 3X5 Canada Adv CERT Canada Toronto ON Canada

Background and Objective As the most common cardiovascular defect, coronary artery disease (CAD), also called ischemic heart disease, is one of the substantial causes of death globally. Several diagnosis approaches such as baseline electrocardiography, echocardiography, magnetic resonance imaging, and coronary angiography are suggested for screening the suspected patients that may suffer from CAD. However, applying such methods may have health side effects and/or expensive *** As an alternative to the available diagnosis tools/methods, this research involves a decision tree learning algorithm called classification and regression tree (CART) for a simple and reliable diagnosis of CAD. Several CART models are developed based on the recently CAD dataset published in the *** Utilizing all the features of the dataset (55 independent parameters), it was found that only 40 independent parameters influence the CAD diagnosis and consequently development of the predictive model. Based on the feature importance obtained from the first CART model, three new CART models are then developed using 18, 10, and 5 selected features. Except for the five-feature CART model, the outcomes of developed CART models demonstrate the maximum achievable accuracy, sensitivity, and specificity for CAD diagnosis (100%), while comparing the predictions with the reported targets. The error analysis reveals that the literature models including sequential minimal optimization (SMO), bagging SMO, Naïve Bayes (NB), artificial neural network (ANN), C4.5, J48, Bagging, and ANN in conjunction with the genetic algorithm (GA) do not outperform the CART methodology in classifying patients as normal or *** Hence, the robustness of the tree-based algorithm in accurate and fast predictions is confirmed, implying the proposed classification technique can be successfully utilized to develop a coherent decision-making system for the CAD diagnosis.

关键词： Coronary artery disease classification Decision tree Error analysis classification and regression tree

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A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions

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ENERGY 2020年第0期197卷 117239-000页

作者： Alizamir, Meysam Kim, Sungwon Kisi, Ozgur Zounemat-Kermani, Mohammad Islamic Azad Univ Dept Civil Engn Hamedan Branch Hamadan Iran Dongyang Univ Dept Railrd Construct & Safety Engn Yeongju South Korea Ilia State Univ Dept Civil Engn Tbilisi Georgia Shahid Bahonar Univ Kerman Dept Water Engn Kerman Iran

In this study, the potential of six different machine learning models, gradient boosting tree (GBT), multilayer perceptron neural network (MLPNN), two types of adaptive neuro-fuzzy inference systems (ANFIS) based on fuzzy c-means clustering (ANFIS-FCM) and subtractive clustering (ANFIS-SC), multi-variate adaptive regression spline (MARS), and classification and regression tree (CART) were used for forecasting solar radiation from two stations of two different locations, Turkey and USA. Wind speed, maximum air temperature, minimum air temperature and relative humidity were used as inputs to the developed models. For accurate evaluation of performance of models, four statistical indicators, root mean squared error (RMSE), coefficient of correlation (R), mean absolute error (MAE) and Nash-Sutcliffe efficiency coefficient (NS) were employed to evaluate accuracy of the developed models. Comparison of results showed that the GBT model performed better than the MLPNN, ANFIS, MARS, and CART in modeling solar radiation. The average RMSE of MLPNN, ANFIS-FCM, ANFIS-SC, MARS and CART models was decreased by 0.26%, 1.5%, 0.51%, 2.5%, and 19.34% using GBT model at Fairfield Station, 4%, 1.37%, 0.24%, 4.12%, and 24.4% at Monmouth Station, 11.99%, 48.7%, 41.6%, 8.23%, and 33.41% at Antalya Station, 11%, 54.8%, 51.9%, 19.65%, and 37.1% at Mersin Station, respectively. The overall results indicated that the GBT model could be successfully applied in forecasting solar radiation by using climatic parameters as inputs. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Solar radiation Gradient boosting tree Artificial neural network Adaptive neuro fuzzy inference system classification and regression tree

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Surface Motion Prediction and Mapping for Road Infrastructures Management by PS-InSAR Measurements and Machine Learning Algorithms

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REMOTE SENSING 2020年第23期12卷 3976-3976页

作者： Fiorentini, Nicholas Maboudi, Mehdi Leandri, Pietro Losa, Massimo Gerke, Markus Univ Pisa Dept Civil & Ind Engn Engn Sch Largo Lucio Lazzarino 1 I-56126 Pisa Italy Tech Univ Carolo Wilhelmina Braunschweig Inst Geodesy & Photogrammetry Bienroder Weg 81 D-38106 Braunschweig Germany

This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning Algorithms (MLAs) are employed for achieving the purpose. Two single learners, i.e., regression tree (RT) and Support Vector Machine (SVM), and two ensemble learners, i.e., Boosted regression trees (BRT) and Random Forest (RF) are utilized for estimating the surface motion ratio in terms of mm/year over the Province of Pistoia (Tuscany Region, central Italy, 964 km(2)), in which strong subsidence phenomena have occurred. The interferometric process of 210 Sentinel-1 images from 2014 to 2019 allows exploiting the average displacements of 52,257 Persistent Scatterers as output targets to predict. A set of 29 environmental-related factors are preprocessed by SAGA-GIS, version 2.3.2, and ESRI ArcGIS, version 10.5, and employed as input features. Once the dataset has been prepared, three wrapper feature selection approaches (backward, forward, and bi-directional) are used for recognizing the set of most relevant features to be used in the modeling. A random splitting of the dataset in 70% and 30% is implemented to identify the training and test set. Through a Bayesian Optimization Algorithm (BOA) and a 10-Fold Cross-Validation (CV), the algorithms are trained and validated. Therefore, the Predictive Performance of MLAs is evaluated and compared by plotting the Taylor Diagram. Outcomes show that SVM and BRT are the most suitable algorithms;in the test phase, BRT has the highest Correlation Coefficient (0.96) and the lowest Root Mean Square Error (0.44 mm/year), while the SVM has the lowest difference between the standard deviation of its predictions (2.05 mm/year) and that of the reference samples (2.09 mm/year). Finally, algorithms are used for mapping surface motion over the study area. We propose thre

关键词： infrastructure monitoring PS-InSAR machine learning bayesian optimization algorithm classification and regression tree support vector machine boosted regression tree random forest Taylor diagram surface motion

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Rockburst assessment in deep geotechnical conditions using true-triaxial tests and data-driven approaches

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INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES 2020年 128卷 104279-104279页

作者： Faradonbeh, Roohollah Shirani Taheri, Abbas Ribeiro e Sousa, Luis Karakus, Murat Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia State Key Lab Geomech & Deep Underground Engn Beijing Peoples R China Univ Porto Porto Portugal

Deep underground excavations in mining and civil engineering are subjected to high in-situ stresses which can cause rockburst. Rockburst is an instantaneous release of a large amount of strain energy stored in rockmass that can lead to injuries, deaths, and damage to infrastructures. Many studies have been done regarding rockburst, however, there is no practical model to predict the stress level that rockburst occurs (i.e. maximum rockburst stress) and its related risk (i.e. rockburst risk index) based on real rockburst tests, and the main rock mechanical properties. In this study, a comprehensive database of true-triaxial unloading tests on rocks having a wide range of properties was compiled. The agglomerative hierarchical clustering (AHC) analysis was carried out on the original database to evaluate the presence of natural groups and outliers. Then, the stepwise selection and elimination (SSE) procedure were employed for dimension reduction of the problem and identifying the most influential attributes on rockburst parameters. Afterward, two robust non-linear algorithms, including gene expression programming (GEP) and classification and regression tree (CART) were used to develop the predictive models for rockburst maximum stress and its risk index. The validation verification of the proposed models using several indices proved the high prediction performance of the developed non-linear models. Finally, a parametric analysis was carried out to evaluate the influence of each input parameter on the corresponding output. The proposed models in this study are practical and do not require any presupposition about rockburst mechanism, which makes them be used easily in practice by engineers at the design and progress stages of the underground projects.

关键词： Rockburst maximum stress Rockburst risk index True-triaxial test Gene expression programming classification and regression tree

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Reservoir facies classification based on random forest and geostatistics methods in an offshore oilfield

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JOURNAL OF APPLIED GEOPHYSICS 2022年 201卷

作者： Rahimi, Mehran Riahi, Mohammad Ali Shahid Chamran Univ Ahvaz Fac Earth Sci Ahvaz Iran Univ Tehran Inst Geophys Tehran Iran

Machine learning methods are increasingly employed in various seismic and petrophysical methods for parameter estimation, interpretation, prediction, and classification. Reservoir facies classification assists the interpretation of seismic data as an important step in petroleum exploration and production monitoring. In this study, we estimate a reservoir facies model by integrating random forest (RF) algorithms and geostatistics modeling. The Surmeh Formation with the Jurassic age is known as one of the most important hydrocarbon reservoirs in the Middle East. The upper part of the Surmeh Formation is equivalent to the Arab Formation, which includes sequences of evaporitic carbonate facies in the study area. Well log data including DT, GR, RHOB, and PHI are used in the RF method for reservoir facies classification. Cross-validation verifies the high accuracy of our classification, with an average accuracy of 95%. The predicted reservoir facies consistently describe the carbonate and evaporitic facies with the geological information of this formation. The decision tree diagrams of the RF algorithm give valuable information on decision limitations and how to select features for efficient computation. We use the classification results for facies modeling. The comparison between facies models and drilling core data shows that the APE value of the sequential indicator simulation model is less than that of the indicator kriging model.

关键词： classification and regression tree Decision tree Geostatistics Machine learning Random forest Surmeh Formation

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Insight into structural composition of dissolved organic matter in saline-alkali soil by fluorescence spectroscopy coupled with self-organizing map and structural equation modeling

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022年 279卷 121311页

作者： Liu, Dongping Yu, Huibin Gao, Hongjie Liu, Xueyu Xu, Weining Yang, Fang Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Dayangfang 8 Beijing 100012 Peoples R China Beijing Normal Univ Coll Water Sci Beijing 100875 Peoples R China

Soil salinization has been occurring all over the world, which severely affected crop production and threatened the life of mankind. It is necessary to take serious steps to improve soil fertility for the sustainability and productive capacity of agriculture. Soil samples of different depths were collected from native vegetation communities (Comm. Phragmites communis (CPC) and Comm. Populus alba (CPA)) and irrigated crops (corn fields (CFD) and seed melon fields (SMF)) in Hetao irrigation area of China. Three dimensional excitation-emission matrix (EEM) fluorescence technology combined with self-organizing map were used to analyze the dissolved organic matter (DOM) composition and structural characteristics in saline-alkali soils and its spatial distribution under different vegetation covers. Critical factors were recognized by classification and regression tree (CART) for distinguishing soil samples, and latent factors were revealed with structural equation modeling (SEM) for improving the humification degree of DOM from saline soils in Hetao irrigation area. Five components were obtained in the DOM substances, i.e., tyrosine-like (C1), tryptophan-like (C2), UV fulvic-like (C3), visible fulvic-like (C4) and humic-like (C5). The protein-like peaks were all obvious, and the fulvic-like peaks (600-735 a.u.) were conspicuous in the CPC soil than in others, except CFD1 and SMF1. C1 was the critical factor to distinguish native vegetation from irrigated crops, and C1 and C2 were the critical factors to distinguish CFD from SMF. Contrary to the HA/FA (0.20) and A/C (0.25), the path coefficient (-0.15) of sources with T/H was negative, indicating that the incremental contents of fluorenscense substances were in the sequences of protein-like > visible fulvic-like > UV fulvic-like > humic-like, affecting by the allochthonous. C1 (1.00) and C4 (1.00) were the primary components for improving the humification degree of DOM, which were principally originated from plant debris.

关键词： Soil dissolved organic matter Fluorescence spectroscopy Self-organizing map classification and regression tree Structural equation modeling

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