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Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset
2018
Symmetry
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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
2014
Journal of Information Processing Systems
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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
2019
International Journal of Information and Communication Sciences
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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
2020
IEEE Access
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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
2020
Applied Sciences
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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
2022
Risks
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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
2022
IEEE Access
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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]
2019
arXiv
pre-print
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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
2018
International Journal on Electrical Engineering and Informatics
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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
2019
IAES International Journal of Artificial Intelligence (IJ-AI)
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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]
2022
arXiv
pre-print
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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]
2021
Frontiers in Artificial Intelligence and Applications
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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
2021
Mathematics
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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)
2021
مجلة الاسکندریة للبحوث المحاسبیة
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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
2022
International Journal of Advanced Computer Science and Applications
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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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