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A Novel Ensemble Credit Scoring Model Based on Extreme Learning Machine and Generalized Fuzzy Soft Sets

Dayu Xu, Xuyao Zhang, Junguo Hu, Jiahao Chen
2020 Mathematical Problems in Engineering  
Thirdly, a novel ensemble strategy is proposed to make the imbalanced training data set balanced for each extreme learning machine (ELM) classifier.  ...  This paper mainly discusses the hybrid application of ensemble learning, classification, and feature selection (FS) algorithms simultaneously based on training data balancing for helping the proposed credit  ...  ELM model was employed as basic classifier, and a novel ensemble strategy was generated to make the imbalanced training data sets become balanced for each ELM classifier.  ... 
doi:10.1155/2020/7504764 fatcat:f3kjisulhvhkjnlhzqjicj427e

Adaptively Promoting Diversity in a Novel Ensemble Method for Imbalanced Credit-Risk Evaluation

Yitong Guo, Jie Mei, Zhiting Pan, Haonan Liu, Weiwei Li
2022 Mathematics  
As for most credit-risk evaluation scenarios in the real world, only imbalanced data are available for model construction, and the performance of ensemble models still needs to be improved.  ...  imbalanced data sets.  ...  As Table 9 shows, we only propose an imbalanced ensemble method for these seven data sets. The table illustrates that SA-DP Forest maintained a good performance on different applications.  ... 
doi:10.3390/math10111790 fatcat:n6yqiw6fozfa5bkao5jtogwgvm

Online Learning Method for Drift and Imbalance Problem in Client Credit Assessment

Hang Zhang, Qingbao Liu
2019 Symmetry  
Aiming at solving the joint research issue of concept drift and class imbalance in client credit assessments, in this paper, a novel sample-based online learning ensemble (SOLE) for client credit assessment  ...  A novel multiple time scale ensemble classifier and a novel sample-based online class imbalance learning procedure are proposed to handle the potential concept drift and class imbalance in the client credit  ...  First, a novel multiple time scale ensemble classifier is proposed that contains a stable classifier and dynamic classifiers.  ... 
doi:10.3390/sym11070890 fatcat:e3avij4re5a2hpcmm2xxsfgqtq

Bagging Supervised Autoencoder Classifier for Credit Scoring [article]

Mahsan Abdoli, Mohammad Akbari, Jamal Shahrabi
2021 arXiv   pre-print
Credit scoring models, which are among the most potent risk management tools that banks and financial institutes rely on, have been a popular subject for research in the past few decades.  ...  of loan applicants that can be regarded as a positive development in credit scoring models.  ...  Xiao et al. (2012) proposed a cost-sensitive dynamic ensemble method that dynamically selects classifiers for each test sample classification.  ... 
arXiv:2108.07800v1 fatcat:y332jv6nqjalrgnqttf2saqwjm

A clustering and selection based transfer ensemble model for customer credit scoring

Jin Xiao, Ling Xie, Dunhu Liu, Yi Xiao, Yi Hu
2016 Filomat  
Customer credit scoring is an important concern for numerous domestic and global industries.  ...  This study combines ensemble learning with transfer learning, and proposes a clustering and selection based transfer ensemble (CSTS) model to transfer the instances from related source domains to target  ...  The existing ensemble strategies can be divided into two types: 1) static classifier ensemble (SCE) [22] , which selects a unified ensemble scheme for all test patterns; 2) dynamic classifier ensemble  ... 
doi:10.2298/fil1615015x fatcat:g2z7irhj6bhaxke7fpzouw36ji

An Online Transfer Learning Framework With Extreme Learning Machine for Automated Credit Scoring

Rana Alasbahi, Xiaolin Zheng
2022 IEEE Access  
Furthermore, the framework proposes classifier aggregation and the chunk balancing mechanism for handling class imbalance.  ...  Automated Credit Scoring (ACS) is the process of predicting user credit based on historical data.  ...  INTRODUCTION Managing credit risk and supporting credit application decision-making has become a demanding artificial intelligence and machine learning application.  ... 
doi:10.1109/access.2022.3171569 fatcat:6bavbx7c7fcrdf75qwrrg73xgm

Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model [article]

Anahita Namvar, Mohsen Naderpour
2018 arXiv   pre-print
This paper proposes an innovative credit risk prediction framework that fuses base classifiers based on a Choquet fuzzy integral.  ...  Indeed, even a small improvement in credit risk prediction would be of benefit to P2P lending platforms.  ...  The proposed ensemble model for credit risk assessment not only improves base classifiers but also outperforms other fusion techniques.  ... 
arXiv:1804.10796v1 fatcat:e4b4bmicanc5jardbuue4oduzm

Ten-year evolution on credit risk research: a systematic literature review approach and discussion

Fernanda Medeiros Assef, Maria Teresinha Arns Steiner
2020 Ingeniería e Investigación  
Among the results, it was found that machine learning is being extensively applied in Credit Risk Assessment, where applications of Artificial Intelligence (AI) were mostly found, more specifically Artificial  ...  In this work, a systematic literature review is proposed which considers both "Credit Risk" and "Credit risk" as search parameters to answer two main research questions: are machine learning techniques  ...  Acknowledgments This study was partially funded by PUCPR and by the Coordination for the Improvement of Education Personnel -Brazil (CAPES, represented by thefirst author) and by the National Council for  ... 
doi:10.15446/ing.investig.v40n2.78649 doaj:49fab6209b7f4390938e44fa1c83b518 fatcat:tm5glc2tz5hddmlfqaznc5na4q

A Wide Scale Classification of Class Imbalance Problem and its Solutions: A Systematic Literature Review

Gillala Rekha, Amit Kumar Tyagi, V. Krishna Reddy
2019 Journal of Computer Science  
Imbalanced data poses a great challenge to (both) data mining and machine learning algorithms.  ...  If prediction is performed by these learning algorithms on imbalanced data, the accuracy will be high for majority classes, i.e., resulting in poor performance.  ...  The authors would like to thank Koneru Lakshmaiah Education Foundation and AARIN, India, an education foundation body and a research network for supporting the project through its financial assistance.  ... 
doi:10.3844/jcssp.2019.886.929 fatcat:cg3x36g4rzhybi7xzca6rfyaqi

Resample-based Ensemble Framework for Drifting Imbalanced Data Streams

Hang Zhang, Weike Liu, Shuo Wang, Jicheng Shan, Qingbao Liu
2019 IEEE Access  
This paper proposes a Resample-based Ensemble Framework for Drifting Imbalanced Stream (RE-DI).  ...  First, a time-decayed strategy decreases the weights of the dynamic classifiers to make the ensemble classifier focus more on the new concept of the data stream.  ...  PAKDD predicts credit card fraud cases from a large amount of transaction records. GMSC is a credit scoring data stream which is used for risk assessment in loan.  ... 
doi:10.1109/access.2019.2914725 fatcat:qozmrqsg4vczhbi6brfwoxd3o4

Dynamic Nearest Neighbor: An Improved Machine Learning Classifier and Its Application in Finances

Oscar Camacho-Urriolagoitia, Itzamá López-Yáñez, Yenny Villuendas-Rey, Oscar Camacho-Nieto, Cornelio Yáñez-Márquez
2021 Applied Sciences  
The support for the applications of learning techniques in topics related to economic risk assessment, among other financial topics of interest, is relevant for us as human beings.  ...  The content of this paper consists of a proposal of a new supervised learning algorithm and its application in real world datasets related to finance, called D1-NN (Dynamic 1-Nearest Neighbor).  ...  y Posgrado, Centro de Investigación en Computación, and Centro de Innovación y Desarrollo Tecnológico en Cómputo), the Consejo Nacional de Ciencia y Tecnología, and Sistema Nacional de Investigadores for  ... 
doi:10.3390/app11198884 fatcat:abwuvfdi4rhnbodi2qeqnaexdm

Active Learning for Imbalanced Ordinal Regression

Jiaming Ge, Haiyan Chen, Dongfang Zhang, Xiaye Hou, Ligang Yuan
2020 IEEE Access  
A co-selecting method is proposed in [26] which uses twofeature-subspace classifiers to choose the balanced samples by adjusting a sampling strategy dynamically from imbalanced sentiment data.  ...  Then we design a sampling strategy of active learning for OR, and then we adjust the sampling strategy dynamically to get a more valuable and balanced training set from imbalanced data.  ... 
doi:10.1109/access.2020.3027764 fatcat:wz3heshqyngtfj43inc7f5njia

Survey on Highly Imbalanced Multi-class Data

Mohd Hakim Abdul Hamid, Marina Yusoff, Azlinah Mohamed
2022 International Journal of Advanced Computer Science and Applications  
Finally, for highly imbalanced multi-class data, this paper presents a novel framework.  ...  data; (3) construct a framework of highly imbalanced multi-class data.  ...  He would like to express his gratitude to the Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) and Universiti Teknologi MARA (UiTM) for providing him with the chance to pursue his  ... 
doi:10.14569/ijacsa.2022.0130627 fatcat:fzzmjmfvsfczhbgmgcg5rwg2ze

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

Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning [article]

Damien Dablain, Bartosz Krawczyk, Nitesh Chawla
2022 arXiv   pre-print
., gender, race, or age) that are often under-represented in the data. We postulate that this problem of under-representation has a corollary to the problem of imbalanced data learning.  ...  For example, one class (those receiving credit) may be over-represented with respect to another class (those not receiving credit) and a particular group (females) may be under-represented with respect  ...  Ensembles find their natural application in learning from imbalanced data, as they leverage the predictive power of multiple learners.  ... 
arXiv:2207.06084v1 fatcat:wyl2wkdmmbfwjgqfgq2okbpbsu
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