The Application of Tree-based model to Unbalanced German Credit Data Analysis

Chen, Zhengye

doi:10.1051/matecconf/201823201005

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…As Chen [5] mentioned that because of the inaccuracy and inefficiency of traditional credit estimation methods, authors intended to build effective models to discriminate low-credit people from high-credit people in unbalanced data. In this condition, the paper tested four tree-based machine learning algorithms to the database, which are Random Forest, Bagging, Decision Tree, and Adaboost.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating Various Machine Learning Techniques in Credit Risk Area

Li¹

2023

BCPBM

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Implementing machine learning techniques to credit scoring is a popular method, which is widely used by many financial institutions and banks at present. As the fast development of machine learning tools, these technologies could provide people more accurate predictions and help enterprises avoid future risk. A supervised machine learning technique is utilized in this research as the classification approach. In this experiment, several machine learning algorithms will be compared in order to present the performance by evaluating the type of credit risk. The data is about assessing customers of a German banking systems from the UCI Machine Learning Repository, which contains 5000 instances and 21 attributes. The final result of this research shows the comparison of 12 scenarios among different combinations of balancing methods, feature selection methods, and predictive algorithms, which finally presents that the collection of Adaptive Synthetic, Boruta and k-Nearest Neighbor receives the highest accuracy score.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluating Various Machine Learning Techniques in Credit Risk Area

Li¹

2023

BCPBM

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“…Supervised and unsupervised discretization methods are among the most used methods for studies that use tree-based classification algorithms. This has led to several arguments on the necessity of applying discretization method on tree-based classification algorithms since only little efforts of discretization are required and yet the output does not give significant impact on classification accuracy [13], [14].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Fuzzy Discretization’s Output on Classification Accuracy of Random Forest Classifier

Fikri¹

2020

IJATCSE

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Random Forest is known as among the widely used classification algorithms by researchers and machine learning enthusiast in solving classification problems. Recently, fuzzy discretization has been paired with Random Forest (RF) classifier to enhance the classification accuracy of Random Forest classifier when dealing with continuous variables. However, there are many different opinions on whether there is a need to perform discretization in data pre-processing for tree-based classifiers such as J48, Decision Tree and Random Forest. On top of that, it is known that different classification algorithms produce different classification accuracies depending on the type of data used. In other words, the output of data discretization process. Thus, to unravel this mentioned hypothesis, this study intends to shed some lights on the impact of different fuzzy discretization's output on the classification accuracy of Random Forest classifier. In this study, three version of simulations were done with different fuzzy discretization output. Those fuzzy discretization's outputs are 1) without fuzzy discretization 2) with fully fuzzy discretization and 3) with partial fuzzy discretization. Then, classification phase is done through Random Forest classifier and the classification accuracy for all the simulation versions were observed, recorded, and analyzed.

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An Empirical Study of Fuzzy Decision Tree for Gradient Boosting Ensemble

Liu

Zhang

et al. 2022

Lecture Notes in Computer Science

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The Application of Tree-based model to Unbalanced German Credit Data Analysis

Cited by 4 publications

References 18 publications

Evaluating Various Machine Learning Techniques in Credit Risk Area

Evaluating Various Machine Learning Techniques in Credit Risk Area

The Impact of Fuzzy Discretization’s Output on Classification Accuracy of Random Forest Classifier

An Empirical Study of Fuzzy Decision Tree for Gradient Boosting Ensemble

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