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A Bayesian latent variable model with classification and regression tree approach for behavior and credit scoring

KNOWLEDGE-BASED SYSTEMS 2012年 36卷 245-252页

作者： Kao, Ling-Jing Chiu, Chih-Chou Chiu, Fon-Yu Natl Taipei Univ Technol Dept Business Management Taipei Taiwan Natl Taipei Univ Technol Inst Commerce Automat & Management Taipei Taiwan

A Bayesian latent variable model with classification and regression tree approach is built to overcome three challenges encountered by a bank in credit-granting process. These three challenges include (1) the bank wants to predict the future performance of an applicant accurately;(2) given current information about cardholders' credit usage and repayment behavior, financial institutions would like to determine the optimal credit limit and APR for an applicant;and (3) the bank would like to improve its efficiency by automating the process of credit-granting decisions. Data from a leading bank in Taiwan is used to illustrate the combined approach. The data set consists of each credit card holder's credit usage and repayment data, demographic information, and credit report. Empirical study shows that the demographic variables used in most credit scoring models have little explanatory ability with regard to a cardholder's credit usage and repayment behavior. A cardholder's credit history provides the most important information in credit scoring. The continuous latent customer quality from the Bayesian latent variable model allows considerable latitude for producing finer rules for credit granting decisions. Compared to the performance of discriminant analysis, logistic regression, neural network, multivariate adaptive regression splines (MARS) and support vector machine (SVM), the proposed model has a 92.9% accuracy rate in predicting customer types, is less impacted by prior probabilities, and has a significantly low Type I errors in comparison with the other five approaches. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Behavior scoring Credit scoring Bayesian Latent variable model classification and regression tree

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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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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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A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models

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EXPERT SYSTEMS WITH APPLICATIONS 2005年第1期29卷 65-74页

作者： Razi, MA Athappilly, K Western Michigan Univ Haworth Sch Business Dept Business Informat Syst Kalamazoo MI 49008 USA

Numerous articles comparing performances of statistical and Neural Networks (NNs) models are available in the literature, however, very few involved classification and regression tree (CART) models in their comparative studies. We perform a three-way comparison of prediction accuracy involving nonlinear regression, NNs and CART models using a continuous dependent variable and a set of dichotomous and categorical predictor variables. A large dataset on smokers is used to run these models. Different prediction accuracy measuring procedures are used to compare performances of these models. The outcomes of predictions are discussed and the outcomes of this research are compared with the results of similar studies. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： neural networks regression classification and regression tree

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Differentiation of lacrimal gland tumors using the multi-model MRI: classification and regression tree (CART)-based analysis

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ACTA RADIOLOGICA 2022年第7期63卷 923-932页

作者： Li, Xiaofeng Wu, Xue Qian, Jiang Yuan, Yifei Wang, Shenjiang Ye, Xinpei Sha, Yan Zhang, Rui Ren, Hui Fudan Univ Dept Ophthalmol Eye & ENT Hosp 83 Fenyang Rd Shanghai 200031 Peoples R China Fudan Univ NHC Key Lab Myopia Shanghai Peoples R China Chinese Acad Med Sci Lab Myopia Shanghai Peoples R China Fudan Univ Dept Radiol Eye & ENT Hosp Shanghai PR Peoples R China

Background Little is known about the value of dynamic contrast-enhanced (DCE) in combination with diffusion-weighted imaging (DWI) for the differentiation of lacrimal gland tumors. Purpose To evaluate the ability of DCE and DWI in differentiating lacrimal gland tumors. Material and Methods DCE and DWI were performed in 72 patients with lacrimal gland tumors. Time-intensity curve (TIC) patterns were categorized as type A, type B, type C, and type D. Apparent diffusion coefficient (ADC) was measured on DWI. Then, the diagnostic effectiveness of TIC in conjunction with ADC was assessed using classification and regression tree (CART) analysis. Results Type A tumors were all epithelial;they could be further separated into pleomorphic adenoma sand carcinomas. Type B tumors were all non-epithelial tumors, which could be further separated into benign inflammatory infiltrates (BIIs) and lymphomas. Type C tumors contained both carcinomas and non-epithelial tumors, which could be diagnosed into carcinomas, BIIs and lymphomas. Type D tumors were all PAs. The mean ADC of epithelial tumors was significantly higher than that of non-epithelial tumors, and the mean ADC values were significantly different between PAs and carcinomas. Besides, the mean ADC value of BIIs was higher than that of lymphomas. Therefore, the CART decision tree made by ADC and TIC had a predictive accuracy of 86.1%, differentiating lacrimal gland tumors effectively. Conclusion Combined DCE and DWI-MRI can efficiently differentiate lacrimal gland tumors which can be of help to ophthalmologists in the diagnosis and treatment of these tumors.

关键词： Eye neoplasms lacrimal apparatus diseases dynamic contrast-enhanced diffusion-weighted imaging classification and regression tree CART

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Mining the customer credit using classification and regression tree and multivariate adaptive regression splines

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2006年第4期50卷 1113-1130页

作者： Lee, TS Chiu, CC Chou, YC Lu, CJ Fu Jen Catholic Univ Grad Inst Measurement Taipei Taiwan Natl Taipei Univ Technol Inst Commerce Automat & Management Taipei Taiwan Fu Jen Catholic Univ Dept Business Adm Taipei Taiwan Ching Yun Univ Dept Ind Engn & Management Taoyuan Taiwan

Credit scoring has become a very important task as the credit industry has been experiencing severe competition during the past few years. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the relative importance of potential input variables, long training process, and interpretative difficulties have often been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring using two commonly discussed data mining techniques-classification and regression tree (CART) and multivariate adaptive regression splines (MARS). To demonstrate the effectiveness of credit scoring using CART and MARS, credit scoring tasks are performed on one bank credit card data set. As the results reveal, CART and MARS outperform traditional discriminant analysis, logistic regression, neural networks, and support vector machine (SVM) approaches in terms of credit scoring accuracy and hence provide efficient alternatives in implementing credit scoring tasks. (c) 2004 Elsevier B.V. All rights reserved.

关键词： credit scoring data mining classification and regression tree multivariate adaptive regression splines support vector machine

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Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia (vol 13, pg 839, 2016)

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LANDSLIDES 2016年第5期13卷 1315-1318页

作者： Youssef, Ahmed Mohamed Pourghasemi, Hamid Reza Pourtaghi, Zohre Sadat Al-Katheeri, Mohamed M. Sohag Univ Fac Sci Dept Geol Sohag Egypt Saudi Geol Survey Appl Geol Sect Geol Hazards Dept POB 54141 Jeddah 21514 Saudi Arabia Shiraz Univ Coll Agr Dept Nat Resources & Environm Engn Shiraz Iran Yazd Univ Dept Environm Management Engn Coll Nat Resources Yazd Iran

The purpose of the current study is to produce landslide susceptibility maps using different data mining models. Four modeling techniques, namely random forest (RF), boosted regression tree (BRT), classification and regression tree (CART), and general linear (GLM) are used, and their results are compared for landslides susceptibility mapping at the Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslide locations were identified and mapped from the interpretation of different data types, including high-resolution satellite images, topographic maps, historical records, and extensive field surveys. In total, 125 landslide locations were mapped using ArcGIS 10.2, and the locations were divided into two groups; training (70?%) and validating (25?%), respectively. Eleven layers of landslide-conditioning factors were prepared, including slope aspect, altitude, distance from faults, lithology, plan curvature, profile curvature, rainfall, distance from streams, distance from roads, slope angle, and land use. The relationships between the landslide-conditioning factors and the landslide inventory map were calculated using the mentioned 32 models (RF, BRT, CART, and generalized additive (GAM)). The models’ results were compared with landslide locations, which were not used during the models’ training. The receiver operating characteristics (ROC), including the area under the curve (AUC), was used to assess the accuracy of the models. The success (training data) and prediction (validation data) rate curves were calculated. The results showed that the AUC for success rates are 0.783 (78.3?%), 0.958 (95.8?%), 0.816 (81.6?%), and 0.821 (82.1?%) for RF, BRT, CART, and GLM models, respectively. The prediction rates are 0.812 (81.2?%), 0.856 (85.6?%), 0.862 (86.2?%), and 0.769 (76.9?%) for RF, BRT, CART, and GLM models, respectively. Subsequently, landslide susceptibility maps were divided into four classes, including low, moderate, high, and very high susceptibility. The results re

关键词： Landslide susceptibility mapping Random forest Boosted regression tree classification and regression tree General linear model Saudi Arabia

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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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Discriminative Ability of Heel Quantitative Ultrasound in Postmenopausal Women with Prevalent Vertebral Fractures: Application of Optimal Threshold Cutoff Values Using classification and regression tree Models

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CALCIFIED TISSUE INTERNATIONAL 2012年第2期91卷 114-120页

作者： del Carmen Navarro, Maria Saavedra, Pedro Jesus Gomez-de-Tejada, Maria Suarez, Mercedes Hernandez, Diego Sosa, Manuel Univ Las Palmas Gran Canaria Invest Grp Osteoporosis & Bone Mineral Dis Las Palmas Gran Canaria 35005 Canary Islands Spain Univ Las Palmas Gran Canaria Grp Invest Educ & Promot Hlth Las Palmas Gran Canaria 35005 Canary Islands Spain Univ Las Palmas Gran Canaria Dept Math Las Palmas Gran Canaria 35005 Canary Islands Spain Univ Seville Dept Med Seville Spain Hosp Univ Insular Bone Metab Unit Las Palmas Gran Canaria Canary Islands Spain Univ Las Palmas Gran Canaria Grp Invest Osteoporosis & Bone Mineral Dis Las Palmas Gran Canaria 35005 Canary Islands Spain

Quantitative ultrasound (QUS) of the heel has been proposed as a screening tool to evaluate the bone status and risk of osteoporotic fragility fractures. The aim of this study was to define threshold values that would maximize the predictive ability of QUS to discriminate subjects with vertebral fractures using the classification and regression trees (CART) models. A cross-sectional analysis was made of a cohort of 1,132 postmenopausal women with a mean age of 58 years. A total of 205 women (18.1 %) presented with a history of vertebral fracture. For all patients, a questionnaire of osteoporosis risk factors was given and measurements of the heel QUS and bone mineral density at the lumbar spine and the proximal femur, obtained by dual-energy X-ray absorptiometry (DXA), were made. Spinal radiographs were assessed for vertebral fractures. Sensitivity, specificity, predictive values, likelihood ratios, and receiver operator characteristics (ROC) curve QUS values were calculated using the optimal threshold identified in the CART models. Cutoff values calculated from best CART model (i.e., a QUS index > 90.5 %) yielded a sensitivity of 80.3 % (95 % CI 69.2-88.1), a negative predictive value of 94 % (95 % CI 90.1-96.5), and a specificity of 68.8 % (95 % CI 63.3-73.8). This cutoff value would obviate the need to perform DXA in 32.8 % of the women of our population at risk for vertebral fractures. The area under the ROC curve of the best model was 0.8071. QUS was shown to discriminate between women with and without a history of vertebral fracture and constitutes a useful tool for assessing vertebral fracture risk. The application of decision trees (CART analyses) was helpful to define the optimal threshold QUS values.

关键词： classification and regression tree Cutoff Densitometry Vertebral fracture Fracture risk Quantitative ultrasound Threshold Osteoporosis Postmenopausal women

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Using classification and regression tree modeling to investigate appetite hormones and proinflammatory cytokines as biomarkers to differentiate bipolar I depression from major depressive disorder

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CNS SPECTRUMS 2022年第4期27卷 450-456页

作者： Huang, Kai-Lin Chen, Mu-Hong Hsu, Ju-Wei Tsai, Shih-Jen Bai, Ya-Mei Taipei Vet Gen Hosp Dept Psychiat Taipei Taiwan Natl Yang Ming Chiao Tung Univ Coll Med Dept Psychiat Taipei Taiwan

Background Altered immunity and metabolic profiles have been compared between bipolar depression (BD) and major depressive disorder (MDD). This study aimed at developing a composite predictor of appetite hormones and proinflammatory cytokines to differentiate BD from MDD. Methods This cross-sectional study enrolled patients with BD and those with MDD aged 20 to 59 years and displaying depressive episodes. Clinical characteristics (age, sex, body mass index, and depression severity), cytokines (C-reactive protein, interleukin [IL]-2, IL-6, tumor necrosis factor [TNF]-alpha, P-selectin, and monocyte chemoattractant protein), and appetite hormones (leptin, adiponectin, ghrelin, and insulin) were assessed as potential predictors using a classification and regression tree (CRT) model for differentiating BD from MDD. Results The predicted probability of a composite predictor of ghrelin and TNF-alpha was significantly greater (for BD: area under curve = 0.877;for MDD: area under curve = 0.914) than that of any one marker (all P > .05) to distinguish BD from MDD. The most powerful predictors for diagnosing BD were high ghrelin and TNF-alpha levels, whereas those for MDD were low ghrelin and TNF-alpha levels. Conclusion A composite predictor of ghrelin and TNF-alpha driven by CRT could assist in the differential diagnosis of BD from MDD with high specificity. Further clinical studies are warranted to validate our results and to explore underlying mechanisms.

关键词： Bipolar disorder major depressive disorder cytokines ghrelin classification and regression tree

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