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Automatic regrouping of trajectories based on classification and regression tree

INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2020年第3期35卷 217-225页

作者： Zhang, Ying Yang, Chenguang South China Univ Technol Coll Automat Sci & Engn Guangzhou 510640 Peoples R China Univ West England Bristol Robot Lab Bristol BS16 1QY Avon England

Decomposing complex tasks into simple sub-trajectories can greatly reduce the difficulty of modelling and generalisation. Using dynamic movement primitive (DMP) to generalise these sub-trajectories and combining the generalised sub-trajectories in a different order can generalise the original task to a new task, which greatly improves the generalisation ability of DMP. In previous work, we manually determined the recombination order of trajectories, but this method was inefficient and time-consuming. Here, we automate the procedure with the decision tree approach. First, we use some known decision results as prior information to generate decision trees. Then, we input the starting and ending coordinates of each sub-trajectory of the new task into the decision tree. The decision tree will make decisions based on the coordinate information and choose which kind of trajectory to generalise to realise the new task. Simulation results are used to verify the effectiveness of the proposed method.

关键词： DMP dynamic movement primitive trajectory segmentation classification and regression tree

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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 107513-107513页

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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Adverse childhood experiences and their relationship to complex health profiles among child welfare-involved children: A classification and regression tree analysis

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HEALTH SERVICES RESEARCH 2019年第4期54卷 902-911页

作者： Brown, Samantha M. Bender, Kimberly Orsi, Rebecca McCrae, Julie S. Phillips, Jon D. Rienks, Shauna Colorado State Univ Sch Social Work 1586 Campus Delivery Ft Collins CO 80523 USA Univ Denver Grad Sch Social Work Denver CO USA Colorado State Univ Social Work Res Ctr Ft Collins CO 80523 USA Univ Chicago Chapin Hall Ctr Children Chicago IL 60637 USA Univ Denver Butler Inst Families Grad Sch Social Work Denver CO USA

Objective To identify the clustering of adverse childhood experiences (ACEs) that best characterize child welfare-involved children with known complex health concerns. Data Source Multi-informant data were obtained from Wave I of the National Survey of Child and Adolescent Well-Being (NSCAW II). Study Design This study used a cross-sectional design and classification and regression tree (CART) analyses. Data Collection Data were collected from families with children, aged birth to 17, investigated for child maltreatment and their child protective services caseworkers, including demographic characteristics of the children, their histories of adversity, and a wide range of health concerns. Principal Findings Results indicate that for children between the ages of six and 17, experiences of physical abuse alone, as well as experiences of physical abuse combined with having a caregiver with mental illness, are most strongly associated with complex health concerns. For children aged 2-5 years, results suggest that caregiver mental illness is a key adverse experience associated with complex health concerns. Conclusions Identifying specific combinations of ACEs may be a critical next step for child- and youth-serving agencies to allow providers to better calculate risk of health problems among children exposed to adversity.

关键词： adverse childhood experiences child welfare classification and regression tree health concerns

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An MRI-based decision tree to distinguish lipomas and lipoma variants from well-differentiated liposarcoma of the extremity and superficial trunk: classification and regression tree (CART) analysis

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EUROPEAN JOURNAL OF RADIOLOGY 2020年 127卷 109012-109012页

作者： Shim, Eun Jung Yoon, Min A. Yoo, Hye Jin Chee, Choong Guen Lee, Min Hee Lee, Sang Hoon Chung, Hye Won Shin, Myung Jin Univ Ulsan Asan Med Ctr Coll Med Dept Radiol 88 Olymp Ro 43 Gil Seoul 05505 South Korea Univ Ulsan Asan Med Ctr Coll Med Res Inst Radiol 88 Olymp Ro 43 Gil Seoul 05505 South Korea Seoul Natl Univ Seoul Natl Univ Hosp Dept Radiol Coll Med 101 Daehak Ro Seoul 03080 South Korea

Purpose: To build and validate a decision tree model using classification and regression tree (CART) analysis to distinguish lipoma and lipoma variants from well-differentiated liposarcoma of the extremities and superficial trunk. Methods: This retrospective study included patients who underwent surgical resection and preoperative contrast-enhanced MR imaging for lipoma, lipoma variants, and well-differentiated liposarcoma in two tertiary referral centers. Six MRI findings (tumor size, anatomical location, tumor depth, shape, enhancement pattern, and presence of intermingled muscle fibers) and two demographic factors (patient age and sex) were assessed to build a classification tree using CART analysis with minimal error cross-validation pruning based on a complexity parameter. Results: The model building cohort consisted of 231 patients (186 lipoma and lipoma variants and 45 well-differentiated liposarcoma) from one center, while the validation cohort consisted of 157 patients (136 lipoma and lipoma variants and 21 well-differentiated liposarcoma) from another center. In the CART analysis, the contrast enhancement pattern (no enhancement or thin septal enhancement versus thick septal, nodular, confluent hazy, or solid enhancement) was the first partitioning predictor, followed by a maximal tumor size of 12.75 cm. The tree model allowed distinction of lipoma and lipoma variants from well-differentiated liposarcoma in both the model building cohort (C-statistics, 0.955;sensitivity 80 %, specificity 94.62 %, accuracy 91.77 %) and the external validation cohort (C-statistics, 0.917;sensitivity 66.67 %, specificity 95.59 %, accuracy 91.72 %). Conclusion: The distinction of lipoma and lipoma variants from well-differentiated liposarcoma can be achieved with the simple classification tree model.

关键词： Lipoma Lipoma variants Well-differentiated liposarcoma MRI classification and regression tree

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classification of driving workload affected by highway alignment conditions based on classification and regression tree algorithm

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TRAFFIC INJURY PREVENTION 2018年第2期19卷 214-218页

作者： Hu, Jiangbi Wang, Ronghua Beijing Univ Technol Coll Architecture & Civil Engn 100 Pingleyuan Beijing Peoples R China

Objective: Guaranteeing a safe and comfortable driving workload can contribute to reducing traffic injuries. In order to provide safe and comfortable threshold values, this study attempted to classify driving workload from the aspects of human factors mainly affected by highway geometric conditions and to determine the thresholds of different workload classifications. This article stated a hypothesis that the values of driver workload change within a certain ***: Driving workload scales were stated based on a comprehensive literature review. Through comparative analysis of different psychophysiological measures, heart rate variability (HRV) was chosen as the representative measure for quantifying driving workload by field experiments. Seventy-two participants (36 car drivers and 36 large truck drivers) and 6 highways with different geometric designs were selected to conduct field experiments. A wearable wireless dynamic multiparameter physiological detector (KF-2) was employed to detect physiological data that were simultaneously correlated to the speed changes recorded by a Global Positioning System (GPS) (testing time, driving speeds, running track, and distance). Through performing statistical analyses, including the distribution of HRV during the flat, straight segments and P-P plots of modified HRV, a driving workload calculation model was proposed. Integrating driving workload scales with values, the threshold of each scale of driving workload was determined by classification and regression tree (CART) ***: The driving workload calculation model was suitable for driving speeds in the range of 40 to 120km/h. The experimental data of 72 participants revealed that driving workload had a significant effect on modified HRV, revealing a change in driving speed. When the driving speed was between 100 and 120km/h, drivers showed an apparent increase in the corresponding modified HRV. The threshold value of the normal driving workload K was bet

关键词： Highway engineering human factors driving workload heart rate variability classification and regression tree

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classification and regression tree analysis of acute-on-chronic hepatitis B liver failure: Seeing the forest for the trees

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JOURNAL OF VIRAL HEPATITIS 2017年第2期24卷 132-140页

作者： Shi, K. -Q. Zhou, Y. -Y. Yan, H. -D. Li, H. Wu, F. -L. Xie, Y. -Y. Braddock, M. Lin, X. -Y. Zheng, M. -H. Wenzhou Med Univ Affiliated Hosp 1 Liver Res Ctr Dept Hepatol Wenzhou Peoples R China Wenzhou Med Univ Inst Hepatol Wenzhou Peoples R China Jinhua Municipal Hosp Dept Cardiol Jinhua Peoples R China Ningbo 2 Hosp Dept Infect Dis Ningbo Zhejiang Peoples R China Tianjin Infect Dis Hosp Dept Intens Care Unit Tianjin Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Dept Clin Lab Wenzhou Peoples R China AstraZeneca R&D Global Med Dev Loughborough Leics England

At present, there is no ideal model for predicting the short-term outcome of patients with acute-on-chronic hepatitis B liver failure (ACHBLF). This study aimed to establish and validate a prognostic model by using the classification and regression tree (CART) analysis. A total of 1047 patients from two separate medical centres with suspected ACHBLF were screened in the study, which were recognized as derivation cohort and validation cohort, respectively. CART analysis was applied to predict the 3-month mortality of patients with ACHBLF. The accuracy of the CART model was tested using the area under the receiver operating characteristic curve, which was compared with the model for end-stage liver disease (MELD) score and a new logistic regression model. CART analysis identified four variables as prognostic factors of ACHBLF: total bilirubin, age, serum sodium and INR, and three distinct risk groups: low risk (4.2%), intermediate risk (30.2%-53.2%) and high risk (81.4%-96.9%). The new logistic regression model was constructed with four independent factors, including age, total bilirubin, serum sodium and prothrombin activity by multivariate logistic regression analysis. The performances of the CART model (0.896), similar to the logistic regression model (0.914, P=.382), exceeded that of MELD score (0.667, P<.001). The results were confirmed in the validation cohort. We have developed and validated a novel CART model superior to MELD for predicting three-month mortality of patients with ACHBLF. Thus, the CART model could facilitate medical decision-making and provide clinicians with a validated practical bedside tool for ACHBLF risk stratification.

关键词： acute-on-chronic hepatitis B liver failure classification and regression tree cohort study model for end-stage liver disease prognostic prediction

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Predicting Ozone Layer Concentration Using Multivariate Adaptive regression Splines, Random Forest and classification and regression tree 7th

Predicting Ozone Layer Concentration Using Multivariate Adap...

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7th International Workshop on Soft Computing Applications (SOFA)

作者： Roy, Sanjiban Sekhar Pratyush, Chitransh Barna, Cornel VIT Univ Sch Comp Sci & Engn Vellore Tamil Nadu India Aurel Vlaicu Univ Arad Automat & Appl Informat Arad Romania

ISBN: (纸本)9783319625249

Air pollution is one of the major environmental worries in recent time. Abrupt increase in the concentration of any gas leads to air pollution. The cities are mostly affected due to the abundance of population there. One of the worst gaseous pollutants is OZONE (O3). In this paper, we propose three predictive models for estimation of concentration of ozone gases in the air which are Random Forest, Multivariate Adaptive regression Splines and classification and regression tree. Evaluation of the prediction models indicates that the Multivariate Adaptive regression Splines model describes the dataset better and has achieved significantly better prediction accuracy as compared to the Random Forest and classification and regression tree. A detailed comparative study has been carried out on the performances of Random Forest, Multivariate Adaptive regression Splines and classification and regression tree. MARS gives the result by considering less variables as compared to other two. Moreover, Random Forest takes a little more time for building the tree as the elapsed time was calculated to 45 s in this case. In addition, variable importance for each model has been predicted. Observing all the graphs Multivariate Adaptive regression Splines gives the closest curve of both train and test set when compared. It can be concluded that multivariate adaptive regression splines can be a valuable tool in predicting ozone for future.

关键词： Ozone Multivariable adaptive regression spline Random Forest 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 1142页

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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Quad-polarized synthetic aperture radar and multispectral data classification using classification and regression tree and support vector machine-based data fusion system

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JOURNAL OF APPLIED REMOTE SENSING 2017年第1期11卷 016007-1-016007-14页

作者： Bigdeli, Behnaz Pahlavani, Parham Univ Tehran Sch Surveying & Geospatial Engn Fac Engn North Kargar St Tehran 111554563 Iran

Interpretation of synthetic aperture radar (SAR) data processing is difficult because the geometry and spectral range of SAR are different from optical imagery. Consequently, SAR imaging can be a complementary data to multispectral (MS) optical remote sensing techniques because it does not depend on solar illumination and weather conditions. This study presents a multisensor fusion of SAR and MS data based on the use of classification and regression tree (CART) and support vector machine (SVM) through a decision fusion system. First, different feature extraction strategies were applied on SAR and MS data to produce more spectral and textural information. To overcome the redundancy and correlation between features, an intrinsic dimension estimation method based on noise-whitened Harsanyi, Farrand, and Chang determines the proper dimension of the features. Then, principal component analysis and independent component analysis were utilized on stacked feature space of two data. Afterward, SVM and CART classified each reduced feature space. Finally, a fusion strategy was utilized to fuse the classification results. To show the effectiveness of the proposed methodology, single classification on each data was compared to the obtained results. A coregistered Radarsat-2 and WorldView-2 data set from San Francisco, USA, was available to examine the effectiveness of the proposed method. The results show that combinations of SAR data with optical sensor based on the proposed methodology improve the classification results for most of the classes. The proposed fusion method provided approximately 93.24% and 95.44% for two different areas of the data. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： synthetic aperture radar multispectral classification and regression tree support vector machine sensor fusion

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Predicting in-hospital mortality in pneumonia-associated septic shock patients using a classification and regression tree: a nested cohort study

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JOURNAL OF INTENSIVE CARE 2018年第1期6卷 66页

作者： Speiser, Jaime L. Karvellas, Constantine J. Shumilak, Geoffery Sligl, Wendy I. Mirzanejad, Yazdan Gurka, Dave Kumar, Aseem Kumar, Anand Wake Forest Sch Med Dept Biostat Sci Div Publ Hlth Sci Winston Salem NC USA Univ Alberta Dept Crit Care Med 1-40 Zeidler Ledcor Bldg Edmonton AB T6G 2X8 Canada Univ Alberta Div Gastroenterol & Hepatol Edmonton AB Canada Univ Alberta Div Crit Care Med & Infect Dis Edmonton AB Canada Univ Manitoba Sect Crit Care Med Winnipeg MB Canada Univ Manitoba Infect Dis Sect Winnipeg MB Canada Surrey Hosp Surrey BC Canada Rush Med Coll Chicago IL 60612 USA Laurentian Univ Sudbury ON Canada

BackgroundPneumonia complicated by septic shock is associated with significant morbidity and mortality. classification and regression tree methodology is an intuitive method for predicting clinical outcomes using binary splits. We aimed to improve the prediction of in-hospital mortality in patients with pneumonia and septic shock using decision tree *** and regression tree models were applied to all patients with pneumonia-associated septic shock in the international, multicenter Cooperative Antimicrobial Therapy of Septic Shock database between 1996 and 2015. The association between clinical factors (time to appropriate antimicrobial therapy, severity of illness) and in-hospital mortality was evaluated. Accuracy in predicting clinical outcomes, sensitivity, specificity, and area under receiver operating curve of the final model was evaluated in training (n=2111) and testing datasets (n=2111).ResultsThe study cohort contained 4222 patients, and in-hospital mortality was 51%. The mean time from onset of shock to administration of appropriate antimicrobials was significantly higher for patients who died (17.2h) compared to those who survived (5.0h). In the training dataset (n=2111), a tree model using Acute Physiology and Chronic Health Evaluation II Score, lactate, age, and time to appropriate antimicrobial therapy yielded accuracy of 73% and area under the receiver operating curve 0.75. The testing dataset (n=2111) had accuracy of 69% and area under the receiver operating curve *** mortality (51%) in patients with pneumonia complicated by septic shock is high. Increased time to administration of antimicrobial therapy, Acute Physiology and Chronic Health Evaluation II Score, serum lactate, and age were associated with increased in-hospital mortality. classification and regression tree methodology offers a simple prognostic model with good performance in predicting in-hospital mortality.

关键词： Pneumonia Septic shock classification and regression tree Antimicrobial therapy

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