scholarly journals Comparison of Discriminant Analysis and Adaptive Boosting Classification and Regression Trees on Data with Unbalanced Class

2021 ◽  
Vol 20 ◽  
pp. 650-656
Author(s):  
Eva Fadilah Ramadhani ◽  
Adji Achmad Rinaldo Fernandes ◽  
Ni Wayan Surya Wardhani
Keyword(s):  
Discriminant Analysis ◽  
Home Ownership ◽  
Class Imbalance ◽  
Secondary Data ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Model Accuracy ◽  
Adaptive Boosting ◽  
Parametric Statistics ◽  
Classification And Regression

This study aims to determine the best classification results among discriminant analysis, CART, and Adaboost CART on Bank X's Home Ownership Credit (KPR) customers. This study uses secondary data which contains notes on the 5C assessment (Collateral, Character, Capacity, Condition, Capital) and collectibility of current and non-current loans. The sample used in this study was from 2000 debtors. Comparison of classifications based on model accuracy, sensitivity, and overall specificity shows that Adaboost CART is the best method for classifying credit collectibility at Bank X. This is due to the class imbalance in the data. This study compares the classification results between parametric statistics, namely discriminant analysis and non-parametric statistics, namely CART and Adaboost CART. The results of the research can be used as material for consideration and evaluation for banks in determining the policy for providing credit to prospective borrowers from the classification results of KPR Bank X consumers.

Classification Accuracy of Neural Networks vs. Discriminant Analysis, Logistic Regression, and Classification and Regression Trees

Methodology ◽  
2007 ◽  
Vol 3 (2) ◽  
pp. 47-57 ◽  
Author(s):  
Holmes Finch ◽  
Mercedes K. Schneider
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Quadratic Discriminant Analysis ◽  
Linear Discriminant ◽  
Classification And Regression ◽  
The Impact

Abstract. This paper compares the predictive accuracy of three commonly used parametric methods for group classification, linear discriminant analysis, quadratic discriminant analysis, and logistic regression, with two less common approaches, neural networks and classification and regression trees. The simulation study examined the impact of such factors as inequality of covariance matrices, distribution of predictors, and group size ratio (among others) on the performance of each method. Results indicate that quadratic discriminant analysis always performs as well as the other methods while neural networks behave very similarly to linear discriminant analysis and logistic regression.

Preoperative hematocrit and platelet count are associated with blood loss during spinal fusion for children with neuromuscular scoliosis

2021 ◽  
pp. 175045892096263
Author(s):  
Margaret O Lewen ◽  
Jay Berry ◽  
Connor Johnson ◽  
Rachael Grace ◽  
Laurie Glader ◽  
...  
Keyword(s):  
Platelet Count ◽  
Blood Loss ◽  
Spinal Fusion ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Neuromuscular Scoliosis ◽  
Independent Variables ◽  
Estimated Blood Loss ◽  
Laboratory Results ◽  
Classification And Regression

Aim To assess the relationship of preoperative hematology laboratory results with intraoperative estimated blood loss and transfusion volumes during posterior spinal fusion for pediatric neuromuscular scoliosis. Methods Retrospective chart review of 179 children with neuromuscular scoliosis undergoing spinal fusion at a tertiary children’s hospital between 2012 and 2017. The main outcome measure was estimated blood loss. Secondary outcomes were volumes of packed red blood cells, fresh frozen plasma, and platelets transfused intraoperatively. Independent variables were preoperative blood counts, coagulation studies, and demographic and surgical characteristics. Relationships between estimated blood loss, transfusion volumes, and independent variables were assessed using bivariable analyses. Classification and Regression Trees were used to identify variables most strongly correlated with outcomes. Results In bivariable analyses, increased estimated blood loss was significantly associated with higher preoperative hematocrit and lower preoperative platelet count but not with abnormal coagulation studies. Preoperative laboratory results were not associated with intraoperative transfusion volumes. In Classification and Regression Trees analysis, binary splits associated with the largest increase in estimated blood loss were hematocrit ≥44% vs. <44% and platelets ≥308 vs. <308 × 109/L. Conclusions Preoperative blood counts may identify patients at risk of increased bleeding, though do not predict intraoperative transfusion requirements. Abnormal coagulation studies often prompted preoperative intervention but were not associated with increased intraoperative bleeding or transfusion needs.

scholarly journals Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model

2021 ◽  
Vol 13 (12) ◽  
pp. 2300
Author(s):  
Samy Elmahdy ◽  
Tarig Ali ◽  
Mohamed Mohamed
Keyword(s):  
Machine Learning ◽  
Regional Scale ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Groundwater Potential ◽  
Machine Learning Algorithms ◽  
Conditioning Factors ◽  
Potential Mapping ◽  
Classification And Regression ◽  
Groundwater Potential Mapping

Mapping of groundwater potential in remote arid and semi-arid regions underneath sand sheets over a very regional scale is a challenge and requires an accurate classifier. The Classification and Regression Trees (CART) model is a robust machine learning classifier used in groundwater potential mapping over a very regional scale. Ten essential groundwater conditioning factors (GWCFs) were constructed using remote sensing data. The spatial relationship between these conditioning factors and the observed groundwater wells locations was optimized and identified by using the chi-square method. A total of 185 groundwater well locations were randomly divided into 129 (70%) for training the model and 56 (30%) for validation. The model was applied for groundwater potential mapping by using optimal parameters values for additive trees were 186, the value for the learning rate was 0.1, and the maximum size of the tree was five. The validation result demonstrated that the area under the curve (AUC) of the CART was 0.920, which represents a predictive accuracy of 92%. The resulting map demonstrated that the depressions of Mondafan, Khujaymah and Wajid Mutaridah depression and the southern gulf salt basin (SGSB) near Saudi Arabia, Oman and the United Arab Emirates (UAE) borders reserve fresh fossil groundwater as indicated from the observed lakes and recovered paleolakes. The proposed model and the new maps are effective at enhancing the mapping of groundwater potential over a very regional scale obtained using machine learning algorithms, which are used rarely in the literature and can be applied to the Sahara and the Kalahari Desert.

Reply from Authors re: Michael W. Kattan. Classification and Regression Trees Versus Nomograms: A Bone Scan Positivity Example. Eur Urol 2010;57:559–60

2010 ◽  
Vol 57 (4) ◽  
pp. 560-561
Author(s):  
Alberto Briganti ◽  
Umberto Capitanio ◽  
Nazareno Suardi ◽  
Andrea Gallina ◽  
Patrizio Rigatti ◽  
...  
Keyword(s):  
Bone Scan ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Classification And Regression
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