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Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset

Tuong Le, Mi Lee, Jun Park, Sung Baik
2018 Symmetry  
Due to the number of bankrupt companies compared to that of non-bankrupt companies, bankruptcy prediction faces the problem of imbalanced data.  ...  Experimental results show that using oversampling techniques to balance the dataset in the training stage can enhance the performance of the bankruptcy prediction.  ...  Conflicts of Interest: The authors declare no conflicts of interest.  ... 
doi:10.3390/sym10040079 fatcat:yyul6vph2jgr7ic6phzkvjcaia

Default Prediction for Real Estate Companies with Imbalanced Dataset

Yuan-Xiang Dong, Zhi Xiao, Xue Xiao
2014 Journal of Information Processing Systems  
This lowers the ability of prediction models to distinguish the default sample.  ...  models for imbalanced dataset.  ...  Beaver [16] was the first to apply a univariate model on financial ratios to predict corporate bankruptcy.  ... 
doi:10.3745/jips.04.0002 fatcat:pgkomviosnbbjdebgubdur7uey

A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method

Xiaoxia Wu, Dongqi Yang, Wenyu Zhang, Shuai Zhang
2019 International Journal of Information and Communication Sciences  
The comparative experiment results show that the ensemble learning method has a good effect on improving the performance of the proposed model.  ...  The bankruptcy of manufacturing corporates is an important factor affecting economic stability.  ...  The main purpose of this research is to explore the effect of different sampling methods on the prediction precision of high imbalance datasets.  ... 
doi:10.11648/j.ijics.20190403.12 fatcat:e6c6svbh7nc6pdy2wctsjgfyc4

An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

Talha Mahboob Alam, Kamran Shaukat, Ibrahim A. Hameed, Suhuai Luo, Muhammad Umer Sarwar, Shakir Shabir, Jiaming Li, Matloob Khushi
2020 IEEE Access  
A similar deployment was also done for corporate bankruptcy prediction with the help of Microsoft Azure Machine Learning Studio [53] .  ...  DEPLOYMENT The experimental results review the effect of various degrees of the credit-related imbalanced datasets for training on credit card default prediction model.  ...  TALHA MAHBOOB ALAM was done a bachelor of software engineering from the University of Management and Technology (UMT), Lahore in 2017, and currently completed a master's in computer science from the University  ... 
doi:10.1109/access.2020.3033784 fatcat:zjukbj7j3ngq7dmmr2hegced2m

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  
In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational  ...  Then, the performance of four associative classifiers was analyzed.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app10082779 fatcat:cs4tl7k7gjdzjcbm2wcs7lysny

Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data

Magdalena Brygała
2022 Risks  
The results show that the predictive performance of the logit model based on a balanced sample is more effective compared to the one based on an imbalanced sample.  ...  This paper examines the usefulness of logit regression in forecasting the consumer bankruptcy of households using an imbalanced dataset.  ...  Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods. Knowledge-Based Systems 41: 16–25. [CrossRef] Zhou, Ying, and Taha M. S.  ... 
doi:10.3390/risks10020024 fatcat:bretg4zk6fggrlg2xdetqbuz2i

Business Failure Prediction Based on a Cost-Sensitive Extreme Gradient Boosting Machine

Yao Zou, Changchun Gao, Han Gao
2022 IEEE Access  
However, the highly imbalanced class distribution of financial risk data and the inexplainable of most machine learning-based early distress warning models limit their commercial application.  ...  a cost-sensitive business failure prediction model.  ...  Several works implemented imbalanced corporate BFP by modifying the distribution of ST and NST samples in business failure datasets, and these methods aim to balance business failure datasets by resampling  ... 
doi:10.1109/access.2022.3168857 fatcat:vvuxpdyiy5c6bc6efj5r64g36e

Investigating bankruptcy prediction models in the presence of extreme class imbalance and multiple stages of economy [article]

Sheikh Rabiul Islam, William Eberle, Sheikh K. Ghafoor, Sid C. Bundy, Douglas A. Talbert, Ambareen Siraj
2019 arXiv   pre-print
in the data (i.e., very few samples for the minority class) that degrades the performance of the prediction model.  ...  In the area of credit risk analytics, current Bankruptcy Prediction Models (BPMs) struggle with (a) the availability of comprehensive and real-world data sets and (b) the presence of extreme class imbalance  ...  Background Since the 1960s, research in the area of default prediction and bankruptcy prediction models have focused on corporate bankruptcy prediction.  ... 
arXiv:1911.09858v1 fatcat:6c4l5exqmreipl4l6f4cwngcmi

The Impact of Data Re-Sampling on Learning Performance of Class Imbalanced Bankruptcy Prediction Models

Dilip Singh Sisodia, National Institute of Technology Raipur, Raipur, India, Upasana Verma, National Institute of Technology Raipur, Raipur, India
2018 International Journal on Electrical Engineering and Informatics  
The aim of this paper is to evaluate the effect of data sampling techniques on the performance of learners using real highly imbalanced Spanish bankruptcy dataset.  ...  The performance of quantitative prediction models are evaluated using G-Mean and area under the curve (AUC) measures on the real highly imbalanced data set.  ...  Conclusion In this paper, the effects of data sampling techniques are investigated on the performance of individual and ensemble learners using class imbalanced bankruptcy prediction dataset.  ... 
doi:10.15676/ijeei.2018.10.3.2 fatcat:wdxqsbplmvbwpchv6opomwhgt4

Prediction of Bankruptcy using Big Data Analytics based on Fuzzy c-means Algorithm

Arup Guha
2019 IAES International Journal of Artificial Intelligence (IJ-AI)  
The objective of this paper is to optimise the selected design model of GA-ANN by using Fuzzy C means algorithm to predict corporate bankruptcies by considering different financial ratios of companies  ...  Effectiveness of this method was proved by comparing its accuracy rate with the results of existing method.  ...  The experimental results on imbalanced datasets proved better performance over the previous sampling methods in terms of AUC and F-measure.  ... 
doi:10.11591/ijai.v8.i2.pp168-174 fatcat:xkdcmymr2bbffbgeob3qp7ngcy

Assessing Dataset Bias in Computer Vision [article]

Athiya Deviyani
2022 arXiv   pre-print
We have also compared their performance to the state-of-the-art attribute classifier trained on the FairFace dataset.  ...  We then trained a classifier for each of the augmented datasets and evaluated their performance on the native test set and on external facial recognition datasets.  ...  Chapter 6 Conclusions In this project, we have successfully evaluated the performance of several methods on alleviating the effect of dataset biases on the final model, primarily caused by imbalances  ... 
arXiv:2205.01811v1 fatcat:nerm3uxlbngqfbi7fsll6zjtre

Multi-Class Imbalanced Corporate Bond Default Risk Prediction Based on the OVO-SMOTE-Adaboost Ensemble Model [chapter]

Jie Sun, Jingmei Zhu
2021 Frontiers in Artificial Intelligence and Applications  
We propose a new approach for multi-class imbalanced corporate bond risk prediction based on the OVO-SMOTE-Adaboost ensemble model, which integrates the one-versus one (OVO) decomposition method, the synthetic  ...  Therefore, the OVO-SMOTE-Adaboost (DT) model has satisfying performance of multi-class imbalanced corporate bond default risk prediction and is of great practical significance.  ...  of multi-class imbalanced corporate bond default risk prediction based on the DT classifier.  ... 
doi:10.3233/faia210388 fatcat:3tuwp4azyzeujnlz4fyuqjuwla

Population Diversity Control of Genetic Algorithm Using a Novel Injection Method for Bankruptcy Prediction Problem

Nabeel Al-Milli, Amjad Hudaib, Nadim Obeid
2021 Mathematics  
An injection method is proposed to redistribute the population once 90% of the solutions are located in one cluster.  ...  The obtained results show the ability of the proposed approach to enhance the performance of the machine learning classifiers in the range of 1% to 4%.  ...  Moreover, we would also like to thank the Editors for their generous comments and support during the review process. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/math9080823 fatcat:4shrm5lrjrgadkrg35gsftrota

دور رأی مراقب الحسابات بشأن استمراریة الشرکة وخصائص مراقب الحسابات فی التنبؤ بإفلاس الشرکات: أدلة من الشرکات المقیدة فی البورصة المصریة (باللغة الانجلیزیة)

جیهان محمد علی
2021 مجلة الاسکندریة للبحوث المحاسبیة  
Using an imbalanced sample of 25 technically bankrupt firms and 50 healthy firms, logit models are constructed to examine the information content and the incremental predictive power of audit related disclosures  ...  The primary objective of this study is to investigate the informativeness and the incremental predictive power of audit related disclosures of listed firms for corporate bankruptcy prediction beyond that  ...  Significantly, the majority of literature on corporate bankruptcy prediction focuses on financial ratios analysis as a popular prediction method.  ... 
doi:10.21608/aljalexu.2021.186522 fatcat:vbwt66xii5btticxsa6spc25b4

Survey on Highly Imbalanced Multi-class Data

Mohd Hakim Abdul Hamid, Marina Yusoff, Azlinah Mohamed
2022 International Journal of Advanced Computer Science and Applications  
The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the  ...  Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset.  ...  He would also want to thank his supervisor and co-supervisor, both of whom are co-authors on this study.  ... 
doi:10.14569/ijacsa.2022.0130627 fatcat:fzzmjmfvsfczhbgmgcg5rwg2ze
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