Filters








1,331 Hits in 8.5 sec

A Novel Method for Credit Scoring based on Cost-sensitive Neural Network Ensemble

Wirot Yotsawat, Pakaket Wattuya, Anongnart Srivihok
2021 IEEE Access  
Most existing studies on credit scoring adapted a concept of classifier ensemble for solving an imbalanced dataset.  ...  The experimental results showed that the proposed CS-NNE approach improves the predictive performance over a single neural network based on imbalanced credit datasets, e.g., Thai credit dataset, by achieving  ...  We investigated the performance of single and multiple classifiers for credit scoring utilizing an imbalanced and large dataset.  ... 
doi:10.1109/access.2021.3083490 fatcat:x4moo2t4qjcnxmi6sp72bkth6q

Ensemble Classifier for Solving Credit Scoring Problems [chapter]

Maciej Zięba, Jerzy Świątek
2012 IFIP Advances in Information and Communication Technology  
The goal of this paper is to propose an ensemble classification method for the credit assignment problem. The idea of the proposed method is based on switching class labels techniques.  ...  An application of such techniques allows solving two typical data mining problems: a predicament of imbalanced dataset, and an issue of asymmetric cost matrix.  ...  The classification process refers to an algorithmic procedure for assigning a given input into one of a given classes.  ... 
doi:10.1007/978-3-642-28255-3_7 fatcat:ocfupbgd4ncjpad7kadxkifriq

Efficient Resampling for Fraud Detection During Anonymised Credit Card Transactions with Unbalanced Datasets

Petr Mrozek, John Panneerselvam, Ovidiu Bagdasar
2020 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)  
The key contribution of this paper is the postulation of efficient machine learning algorithms together with suitable resampling methods, suitable for credit card fraud detection with unbalanced dataset  ...  with the motivation of empirically evaluating their efficiencies in handling unbalanced datasets whilst detecting credit card fraud transactions.  ...  Section 3 presents a background on the studied machine learning algorithms, while Section 4 introduces the sampling techniques for imbalanced datasets.  ... 
doi:10.1109/ucc48980.2020.00067 fatcat:4yo7et6yofefjfk5sis4eyhnji

Bagging Supervised Autoencoder Classifier for Credit Scoring [article]

Mahsan Abdoli, Mohammad Akbari, Jamal Shahrabi
2021 arXiv   pre-print
The imbalanced nature of credit scoring datasets, as well as the heterogeneous nature of features in credit scoring datasets, pose difficulties in developing and implementing effective credit scoring models  ...  input data exclusively with regards to the ultimate classification task of credit scoring, based on the principles of multi-task learning.  ...  For instance, Brown & Mues (2012) conducted a study to examine the robustness of several classification methods in classifying imbalanced credit scoring datasets.  ... 
arXiv:2108.07800v1 fatcat:y332jv6nqjalrgnqttf2saqwjm

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

Nhlakanipho Michael Mqadi, Nalindren Naicker, Timothy Adeliyi, Jude Hemanth
2021 Mathematical Problems in Engineering  
The paper aims to provide an in-depth experimental investigation of the effect of using a hybrid data-point approach to resolve the class misclassification problem in imbalanced credit card datasets.  ...  We assessed the proposed method on two imbalanced credit card datasets, namely, the European Credit Card dataset and the UCI Credit Card dataset.  ...  Acknowledgments e authors acknowledge the Durban University of Technology for making funding opportunities and materials for experiments available for this research project.  ... 
doi:10.1155/2021/7194728 fatcat:ims3cvo5l5dpphd6fpmos2yjk4

SGBBA: An Efficient Method for Prediction System in Machine Learning using Imbalance Dataset

Saiful Islam, Umme Sara, Abu Kawsar, Anichur Rahman, Dipanjali Kundu, Diganta Das, A.N.M. Rezaul, Mahedi Hasan
2021 International Journal of Advanced Computer Science and Applications  
A real world big dataset with disproportionate classification is called imbalance dataset which badly impacts the predictive result of machine learning classification algorithms.  ...  The experiments with two benchmark datasets and one highly imbalanced credit card datasets are performed and the performances are compared with the performance of SMOTE resampling method.  ...  ACKNOWLEDGMENTS We are extremely grateful to all co-authors for their strong feedbacks and contributions in the field of learning from imbalanced datasets whereas 1.  ... 
doi:10.14569/ijacsa.2021.0120351 fatcat:oztlnp4drjbjxpkwfcqxtvejoe

Credit risk prediction in an imbalanced social lending environment

Anahita Namvar, Mohammad Siami, Fethi Rabhi, Mohsen Naderpour
2018 International Journal of Computational Intelligence Systems  
In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates  ...  However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial.  ...  The G-mean measure considers both sensitivity and specificity for both classes in calculating its scores and is therefore an effective measure for imbalanced datasets [15] .  ... 
doi:10.2991/ijcis.11.1.70 fatcat:nqcemeizg5bwdd2tgs3kw7m3im

Credit risk prediction in an imbalanced social lending environment [article]

Anahita Namvar, Mohammad Siami, Fethi Rabhi, Mohsen Naderpour
2018 arXiv   pre-print
In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates  ...  However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial.  ...  The G-mean measure considers both sensitivity and specificity for both classes in calculating its scores and is therefore an effective measure for imbalanced datasets [15] .  ... 
arXiv:1805.00801v1 fatcat:jkuzqfgm6beq3f533rigtluqdq

Diversity-Aware Weighted Majority Vote Classifier for Imbalanced Data

Anil Goyal, Jihed Khiari
2020 Zenodo  
In this paper, we propose a diversity-aware ensemble learning based algorithm, referred to as DAMVI, to deal with imbalanced binary classification tasks.  ...  We show efficiency of the proposed approach with respect to state-of-art models on predictive maintenance task, credit card fraud detection, webpage classification and medical applications.  ...  Results Firstly, we report the comparison of our algorithm DAMVI with all the considered baselines in Table II (for F1-score) and Table III (for Average Precision).  ... 
doi:10.5281/zenodo.3956340 fatcat:cgnznpd4ejcurctilmwcyry56i

Diversity-Aware Weighted Majority Vote Classifier for Imbalanced Data [article]

Anil Goyal, Jihed Khiari
2020 arXiv   pre-print
In this paper, we propose a diversity-aware ensemble learning based algorithm, referred to as DAMVI, to deal with imbalanced binary classification tasks.  ...  We show efficiency of the proposed approach with respect to state-of-art models on predictive maintenance task, credit card fraud detection, webpage classification and medical applications.  ...  Results Firstly, we report the comparison of our algorithm DAMVI with all the considered baselines in Table II (for F1-score) and Table III (for Average Precision).  ... 
arXiv:2004.07605v1 fatcat:suvyrskzcrdwjdcnhem5epenne

A MCDM-based evaluation approach for imbalanced classification methods in Financial Risk Prediction

Yongming Song, Yi Peng
2019 IEEE Access  
The traditional practice of using a singular performance metric for classifier evaluation is not sufficient for imbalanced classification.  ...  INDEX TERMS Financial risk prediction, imbalanced classification, multiple criteria decision making (MCDM), algorithm evaluation. YONGMING SONG received the M.S. degree from  ...  Besides, Brown and Mues [5] implemented experimental comparisons with several techniques based on imbalanced credit scoring data sets.  ... 
doi:10.1109/access.2019.2924923 fatcat:mcrphlty6nel3e364uh74urghq

Improved credit scoring model using XGBoost with Bayesian hyper-parameter optimization

Wirot Yotsawat, Pakaket Wattuya, Anongnart Srivihok
2021 International Journal of Power Electronics and Drive Systems (IJPEDS)  
Several state-of-the-art classification algorithms are implemented for predictive comparison with the proposed method.  ...  <span>Several credit-scoring models have been developed using ensemble classifiers in order to improve the accuracy of assessment.  ...  ACKNOWLEDGEMENTS This work was supported by the Department of Computer Science, Faculty of Science, Kasetsart University, Thailand.  ... 
doi:10.11591/ijece.v11i6.pp5477-5487 fatcat:dt7yfzvjg5cjpduxa6qrbecwrm

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Adolfo Rangel-Díaz-de-la-Vega, Yenny Villuendas-Rey, Cornelio Yáñez-Márquez, Oscar Camacho-Nieto, Itzamá López-Yáñez
2020 Applied Sciences  
To do this, six undersampling algorithms, six oversampling algorithms and four hybrid algorithms were evaluated in 13 imbalanced datasets referring to credit scoring.  ...  solutions to the problem of credit scoring.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app10082779 fatcat:cs4tl7k7gjdzjcbm2wcs7lysny

Data Mining for Individual Consumer Credit Default Prediction under E-commence Context: A Comparative Study

Jilei Zhou
2017 International Conference on Information Systems  
Which features plays important roles in credit scoring? (ii) How to tuning classification algorithms in an efficient way to avoid model inefficiency?  ...  The testing results indicated that extreme gradient boosting, a novel ensemble classifier, seems to be very adequate to be used for credit scoring of its good performance under imbalanced credit scoring  ...  In sum up, under the context of imbalance sample, XGBoost resolves imbalance problem by modifying existing classification algorithms to make them appropriate for imbalanced datasets without losing any  ... 
dblp:conf/icis/Zhou17 fatcat:f2rbloapgvgjvoofz3y3n4wu5y

Imbalanced data classification using support vector machine based on simulated annealing for enhancing penalty parameter

Hussein Ibrahim Hussein, Said Amirul Anwar
2021 Periodicals of Engineering and Natural Sciences (PEN)  
The simulated annealing (SA) algorithm is employed to formulate a hybrid method for evaluating SVM parameters.  ...  The choice of these aspects has a substantial impact on the classification precision of SVM as unsuitable parameter settings might drive substandard classification outcomes.  ...  For substantiating the efficacy and usefulness of the recommended approach, precision, recall, and F-score are utilised for evaluating the class-imbalanced classification which utilises the confusion matrix  ... 
doi:10.21533/pen.v9i2.2031 fatcat:by654kd3h5d6jdxwcxspbu5u6y
« Previous Showing results 1 — 15 out of 1,331 results