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The classification and regression tree approach to pump failure rate analysis

RELIABILITY ENGINEERING & SYSTEM SAFETY 2003年第1期79卷 59-67页

作者： Bevilacqua, M Braglia, M Montanari, R Univ Pisa Dipartimento Ingn Meccan Nucl & Prod Fac Ingn I-56126 Pisa Italy Univ Bologna Dipartimento Ingn Costruz Meccan Nucl Aeronaut & I-40136 Bologna Italy Univ Parma Dipartimento Ingn Ind I-43100 Parma Italy

In this article, a technique based on rule induction is suggested as non-parametric alternative to determine the expected failure rates of 143 centrifugal pumps included in a oil refinery plant and subjected to different operating conditions. At the same time, the procedure-makes it possible to determine the critical operating factors influencing the reliability of the pumps. In particular, the classification and regression tree approach is used to automatically generate rules from an extended data base of the plant concerning information about failures and operating conditions of the different facilities. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： failure rate reliability rule induction decision tree classification and regression tree

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Task offloading in mobile fog computing by classification and regression tree

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2020年第1期13卷 104-122页

作者： Rahbari, Dadmehr Nickray, Mohsen Univ Qom Dept Comp Engn & Informat Technol Alghadir Ave Qom Iran

Fog computing (FC) as an extension of cloud computing provides a lot of smart devices at the network edge, which can store and process data near end users. Because FC reduces latency and power consumption, it is suitable for the Internet of Things (IoT) applications as healthcare, vehicles, and smart cities. In FC, the mobile devices (MDs) can offload their heavy tasks to fog devices (FDs). The selection of best FD for offloading has serious challenges in the time and energy. In this paper, we present a Module Placement method by classification and regression tree Algorithm (MPCA). We select the best FDs for modules by MPCA. Initially, the power consumption of MDs are checked, if this value is greater than Wi-Fi's power consumption, then offloading will be done. The MPCA's decision parameters for selecting the best FD include authentication, confidentiality, integrity, availability, capacity, speed, and cost. To optimize MPCA, we analyze and apply the probability of network's resource utilization in the module offloading. This method is called by (MPMCP). To evaluate our proposed approach, we simulate MPCA and MPMCP algorithms and compare them with First Fit (FF) and local mobile processing methods in Cloud, FDs, and MDs. The results include the power consumption, response time and performance show that the proposed methods are superior to other compared methods.

关键词： Mobile fog computing Module placement Task offloading classification and regression tree Markov chain process

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PREDICTION OF RETENTION OF PESTICIDES IN REVERSED-PHASE HIGH-PERFORMANCE LIQUID CHROMATOGRAPHY USING classification and regression tree ANALYSIS AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS

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JOURNAL OF LIQUID CHROMATOGRAPHY & RELATED TECHNOLOGIES 2012年第6期35卷 854-865页

作者： Zarei, Kobra Fatemi, Leila Damghan Univ Sch Chem Damghan 36715364 Iran

A quantitative structure-retention relationship (QSRR) was developed to predict the retention of some pesticides in reversed-phase high-performance liquid chromatography with different mobile phase composition. A set of 1497 zero-to three-dimensional descriptors were used for each molecule in the data set. classification and regression tree (CART) was successfully used as a descriptor selection method. In addition to mobile phase composition, two very simple descriptive descriptors (octanol-water partition coefficient and average molecular weight) were also selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). The root mean square errors for the training, validation, and test sets were 0.027, 0.034, and 0.036, respectively. In comparison with artificial neural network method, the results showed CART-ANFIS is a powerful model for prediction of retention of pesticides.

关键词： adaptive neuro-fuzzy inference system classification and regression tree pesticides quantitative structure-retention relationship retention model reversed-phase liquid chromatography

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A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility

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CATENA 2017年 151卷 147-160页

作者： Chen, Wei Xie, Xiaoshen Wang, Jiale Pradhan, Biswajeet Hong, Haoyuan Bui, Dieu Tien Duan, Zhao Ma, Jianquan Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Peoples R China Univ Putra Malaysia Dept Civil Engn Fac Engn Serdang 43400 Selangor Malaysia Sejong Univ Dept Energy & Mineral Resources Engn 209 Neungdong Ro Seoul 05006 South Korea Jiangxi Meteorol Bur Jiangxi Prov Meteorol Observ 109 ShengfuBeier Rd Nanchang 330046 Jiangxi Peoples R China Univ Coll Southeast Dept Business Adm & Comp Sci Geog Informat Syst Grp N-3800 Bo I Telemark Norway

The main purpose of the present study is to use three state-of-the-art data mining techniques, namely, logistic model tree (LMT), random forest (RF), and classification and regression tree (CART) models, to map landslide susceptibility. Long County was selected as the study area. First, a landslide inventory map was constructed using history reports, interpretation of aerial photographs, and extensive field surveys. A total of 171 landslide locations were identified in the study area. Twelve landslide-related parameters were considered for landslide susceptibility mapping, including slope angle, slope aspect, plan curvature, profile curvature, altitude, NDVI, land use, distance to faults, distance to roads, distance to rivers, lithology, and rainfall. The 171 landslides were randomly separated into two groups with a 70/30 ratio for training and validation purposes, and different ratios of non-landslides to landslides grid cells were used to obtain the highest classification accuracy. The linear support vector machine algorithm (LSVM) was used to evaluate the predictive capability of the 12 landslide conditioning factors. Second, LMT, RF, and CART models were constructed using training data. Finally, the applied models were validated and compared using receiver operating characteristics (ROC), and predictive accuracy (ACC) methods. Overall, all three models exhibit reasonably good performances;the RF model exhibits the highest predictive capability compared with the LMT and CART models. The RF model, with a success rate of 0.837 and a prediction rate of 0.781, is a promising technique for landslide susceptibility mapping. Therefore, these three models are useful tools for spatial prediction of landslide susceptibility. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Logistic model tree Random forest classification and regression tree Landslide China

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Prediction of solubility of some statin drugs in supercritical carbon dioxide using classification and regression tree analysis and adaptive neuro-fuzzy inference systems

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RUSSIAN CHEMICAL BULLETIN 2016年第4期65卷 1131-1138页

作者： Zarei, K. Taheri, F. Damghan Univ Sch Chem Damghan Iran

A quantitative structure-solubility relationship was developed to predict the solubility of some statin drugs in supercritical carbon dioxide (SC-CO2). The solubility of lovastatin, simvastatin, atorvastatin, rosuvastatin, and flovastatin in SC-CO2 at 225 different states of temperature and pressure were predicted. classification and regression tree (CART) was successfully used as a descriptor selection method. Three descriptors (pressure, temperature, and molecular weight) were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). The root mean square errors for the calibration, prediction, and validation sets were 0.09, 0.14, and 0.11, respectively. In comparison with other methods, CART-ANFIS is a powerful model for prediction of solubilities of these statins in SC-CO2.

关键词： classification and regression tree CART adaptive neuro-fuzzy inference system ANFIS statins solubility supercritical carbon dioxide

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External Validation of Two classification and regression tree Models to Predict the Outcome of Inpatient Cardiopulmonary Resuscitation

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JOURNAL OF INTENSIVE CARE MEDICINE 2017年第5期32卷 333-338页

作者： Guilbault, Ryan W. R. Ohlsson, Marcus Andreas Afonso, Anna M. Ebell, Mark H. Mercer Univ Sch Med Macon GA 31207 USA Lund Univ Dept Internal Med Malmo Sweden Duke Univ Sch Med Durham NC USA Univ Georgia Dept Epidemiol & Biostat Coll Publ Hlth Athens GA 30602 USA

Objective: To prospectively validate a previously developed classification and regression tree (CART) model that predicts the likelihood of a good outcome among patients undergoing inpatient cardiopulmonary resuscitation. Design: Prospective validation of a clinical decision rule. Setting: Skane University Hospital in Malmo, Sweden. Patients: All adult patients (N = 287) experiencing in-hospital cardiopulmonary arrest and undergoing cardiopulmonary resuscitation between 2007 and 2010. Interventions: Patients from Skane University Hospital who underwent CPR (N = 287) were classified using the CART models to predict their likelihood of survival neurologically intact or with minimal deficits, based on a cerebral performance category score of 1. Discrimination and classification accuracy of the score in the Swedish population was compared to that in the original (derivation and internal validation) populations. Measurements and Main Results: For model 1, the area under the receiver-operating characteristic curve (AUROCC) was 0.77, compared with 0.76 and 0.73 in the original derivation and validation populations, respectively. Model 1 classified 71 (2.8%) of 287 patients as being at a very low risk of a good neurologic outcome compared with 157 (26.1%) of 287 patients predicted to be at an above average risk of a good neurologic outcome. Model 2 had a similar AUROCC as the original validation population of 0.71 but lower than the original derivation population. Model 2 performed similarly to Model 1 with regards to its ability to correctly classify patients as very low or higher than average likelihood of a good neurologic outcome. Conclusion: Two CART models validated well in a different population, displaying similar discrimination and classification accuracy compared to the original population. Although additional validation in larger populations is desirable before widespread adoption, these results are very encouraging.

关键词： cardiac arrest clinical decision rule cardiopulmonary resuscitation classification and regression tree

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Predicting Early Mortality After Acute Variceal Hemorrhage Based on classification and regression tree Analysis

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CLINICAL GASTROENTEROLOGY AND HEPATOLOGY 2009年第12期7卷 1347-1354页

作者： Augustin, Salvador Muntaner, Laura Altamirano, Jose T. Gonzalez, Antonio Saperas, Esteban Dot, Joan Abu-Suboh, Monder Armengol, Josep R. Malagelada, Joan R. Esteban, Rafael Guardia, Jaime Genesca, Joan Univ Autonoma Barcelona Hosp Univ Vall Hebron Inst Recerca Vall Hebron Liver UnitDept Internal Med Barcelona 08035 Spain Univ Autonoma Barcelona Hosp Univ Vall Hebron Inst Recerca Vall Hebron Dept Gastroenterol Barcelona 08035 Spain Univ Autonoma Barcelona Hosp Univ Vall Hebron Inst Recerca Vall Hebron Digest Endoscopy ServCIBEREHD Barcelona 08035 Spain

BACKGROUND & AIMS: Available prognostic models for mortality after an acute variceal hemorrhage have limitations that restrict their clinical value. We assessed the performance of a novel prognostic approach based on classification and regression tree (CART) analysis. METHODS: Logistic regression (LR) and CART analyses were performed to identify prognostic models for mortality at 6 weeks in a single-center cohort of 267 consecutive patients with acute variceal bleeding. Receiver operating characteristic (ROC) curves were constructed to assess the performance of the models. Prognostic models were fitted and validated by split-sample technique (training set, 164 patients, 2001-2005;test set, 103 patients, 2006-2008). RESULTS: After 6 weeks, 21% of patients experienced rebleeding and 24% died. The best LR model was based on Child-Pugh score, creatinine level, bacterial infection, and hepatocellular carcinoma. CART analysis provided a simple algorithm based on the combined use of just 3 variables (Child-Pugh score, creatinine level, and bacterial infection), allowing accurate early discrimination of 3 distinct prognostic subgroups with 8% (low risk), 17% (intermediate), and 50% to 73% (high) mortality. Its accuracy was similar to the LR model (area under the ROC curves, 0.81 vs 0.84;P = .17) and better than that of Child-Pugh (0.75;P = .05) and model for end-stage liver disease (0.74;P = .05). The prognostic accuracy of both LR and CART models was validated in the test set (area under the ROC curve values, 0.81 and 0.83, respectively). CONCLUSIONS: A simple CART algorithm based on Child-Pugh score, creatinine level, and infection allowed an accurate predictive assessment of 6-week mortality after acute variceal bleeding.

关键词： AUROC area under the receiver operating characteristic curve CART classification and regression tree EST endoscopic sclerotherapy EVL endoscopic variceal ligation HCC hepatocellular carcinoma LR logistic regression MELD Model for End-Stage Liver Disease PI prognostic index R N 2 Nagelkerke's coefficient of determination ROC receiver operating characteristic

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Hydrological regulation ofVibriodynamics in a tropical monsoonal estuary: a classification and regression tree approach

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第1期28卷 724-737页

作者： Krishna, Kiran Veettil, Vipindas Puthiya Anas, Abdulaziz Nair, Shanta Natl Inst Oceanog Reg Ctr CSIR Kochi 682018 Kerala India Natl Ctr Polar & Ocean Res Vasco Da Gama 403804 Goa India Natl Inst Oceanog CSIR Panaji 403004 Goa India

Dynamics ofVibriopopulations in aquatic environments are of concern, as they encompass members pathogenic to humans as well as marine flora and fauna. Spatiotemporal distribution of its culturable abundance for a range of physicochemical and biological parameters in the Cochin estuary (CE), one of the largest tropical monsoonal estuary along the southwest coast of India, witnessed a proliferation of this bacterial group (707 +/- 196 CFU ml(-1)) in downstream stations during a relative dry period. The study for the first time employed classification and regression tree (CART) along with multiple linear regression (MLR) based approaches to explore the nonlinear and linear interactions, respectively, among environmental variables regulatingVibrioabundance in CE. Both the techniques were on consensus to ascertain salinity as the primary determinant ofVibriodynamics, during the entire sampling period regardless of the seasons, viz., dry and wet. Nevertheless, CART outperformed MLR in performance index, suggesting that in a dynamic system like estuaries, usage of the latter is limited by complex nonlinear relationships among environmental variables. According to CART,Vibrioproliferation observed in downstream stations of the estuary (salinity >= 13.4 psu) during a relative dry period was driven by eutrophication (dissolved inorganic phosphate >= 1.48 mu M L-1) associated with reduced flushing resulting in an oxygen-limited environment (dissolved oxygen < 4.56 ml L-1), wherein phytoplankton production diverts to support microbes. Our results imply that anthropogenic activities and sea level rise in future may promptVibrioproliferation, to be a concern for public health and impinge on fisheries yield from tropical estuaries.

关键词： classification and regression tree Estuary Tropics Vibrio Environmental variables Dynamics

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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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Refining the criterion for an abnormal Integrated Relaxation Pressure in esophageal pressure topography based on the pattern of esophageal contractility using a classification and regression tree model

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NEUROGASTROENTEROLOGY AND MOTILITY 2012年第8期24卷 e356-e363页

作者： Lin, Z. Kahrilas, P. J. Roman, S. Boris, L. Carlson, D. Pandolfino, J. E. Northwestern Univ Feinberg Sch Med Dept Med Chicago IL 60611 USA Hosp Civils Lyon Lyon France Univ Lyon 1 F-69365 Lyon France

Background The Integrated Relaxation Pressure (IRP) is the esophageal pressure topography (EPT) metric used for assessing the adequacy of esophagogastric junction (EGJ) relaxation in the Chicago classification of motility disorders. However, because the IRP value is also influenced by distal esophageal contractility, we hypothesized that its normal limits should vary with different patterns of contractility. Methods Five hundred and twenty two selected EPT studies were used to compare the accuracy of alternative analysis paradigms to that of a motility expert (the gold standard). Chicago classification metrics were scored manually and used as inputs for MATLAB (TM) programs that utilized either strict algorithm-based interpretation (fixed abnormal IRP threshold of 15 mmHg) or a classification and regression tree (CART) model that selected variable IRP thresholds depending on the associated esophageal contractility. Key Results The sensitivity of the CART model for achalasia (93%) was better than that of the algorithm-based approach (85%) on account of using variable IRP thresholds that ranged from a low value of >10 mmHg to distinguish type I achalasia from absent peristalsis to a high value of >17 mmHg to distinguish type III achalasia from distal esophageal spasm. Additionally, type II achalasia was diagnosed solely by panesophageal pressurization without the IRP entering the algorithm. Conclusions & Inferences Automated interpretation of EPT studies more closely mimics that of a motility expert when IRP thresholds for impaired EGJ relaxation are adjusted depending on the pattern of associated esophageal contractility. The range of IRP cutoffs suggested by the CART model ranged from 10 to 17 mmHg.

关键词： achalasia classification and regression tree esophageal manometry esophageal motility disorders esophageal pressure topography

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