Large-scale machine learning for business sector prediction

Angenent, Mitch N.; Barata, António Pereira; Takes, Frank W.

doi:10.1145/3341105.3374084

Cited by 7 publications

(3 citation statements)

References 19 publications

(29 reference statements)

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“…Random undersampling was used in previous studies by Angenent et al (2020), Kou et al (2021), Le, Vo, Vo, et al (2019), Liu et al (2022), Wang et al (2018), and Zhou et al (2016), and random oversampling was applied in the studies by Angenent et al (2020), Kou et al (2021), Le, Vo, Vo, et al (2019), and Liu et al (2022), whereas SMOTE was applied in the studies by Farooq and Qamar (2019), Kou et al (2021), Le, Vo, Vo, et al (2019), Liu et al (2022), Roumani et al (2020), Wang et al (2019), and Tumpach et al (2020). The Ovun.sample function from the “ROSE” version 0.0‑3 package in RStudio was used for all three resampling approaches applied in this study.…”

Section: Methodsmentioning

confidence: 99%

Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

Papíková

Papík

2022

Intell Sys Acc Fin Mgmt

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Small and medium-sized enterprises are the pillars of an economy, and their poor performance has a negative impact on living standards of population and country development. This study analyzes real-life data of 89,851 small and medium-sized enterprises, out of which 295 have declared bankruptcy. The analysis is performed via 27 financial ratios. The study framework combines seven classifications and three resampling and seven feature selection methods. Out of all classification methods applied, CatBoost has achieved the best results for all combinations of resampling and feature selection methods. CatBoost surpassed the results of other classification methods for the area under curve parameter, achieving a value of 99.95%. The application of resampling methods on different classification models has not identified a statistically significant level of improvement in any of the resampling methods. This finding has also been observed for feature selection methods. Based on these findings, we assume that individual resampling and feature selection methods do not improve model performance compared with the original imbalanced sample's results.Our results suggest that, even though the data sample may be significantly imbalanced with a minority of bankrupt companies, most classification algorithms can handle this imbalance and achieve interesting results. Moreover, our findings provide broad practical application for all stakeholders who could need to detect bankrupting companies.

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Section: Methodsmentioning

confidence: 99%

Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

Papíková

Papík

2022

Intell Sys Acc Fin Mgmt

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“…Information extracted from other areas, such as clinical registers, that may be combined with police reports of sexual or domestic violence [42,67,68,87] or from managerial and financial reports to support the detection of fraud [49,[105][106][107] are necessary for the enrichment of the analytical process. Combining clinical and police reports can help health professionals understand the causes of trauma that generate psychological illness in victims, supporting a more adjusted treatment, and the police to understand sexual or domestic violence patterns to prevent the occurrence or recurrence of related situations.…”

Section: Scenario Changes and Extensionsmentioning

confidence: 99%

Enabling Public Security Text-Based Analytics: A Survey to Outline Research Directions

De Carvalho,

Dos Santos,

Nepomuceno

et al. 2024

Preprint

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Text mining is a technological trend often highlighted in the continuous exchange of information through interconnected media. Its applicability goes beyond private organizations, as the public sector also requires it to treat textual information regarding services offered. Within this scenario, public security emerges as a prominent user of text mining that seeks to ensure the construction of data and knowledgebases to support decision-making about law enforcement actions to ensure citizen welfare. The primary objectives of this article are: (i) to develop a survey to identify text mining applications, techniques, opportunities, and challenges in public security, and (ii) to outline research directions concerning these topics and provide insights so that interested researchers can develop new studies. The literature was searched within four databases: Scopus, IEEE Xplore, ACM Digital Library, and Web of Science. A filtering process was applied to extract the works most aligned with the target theme, resulting in the selection of 194 of the most relevant works for a literature review. There were identified nineteen key applications of text mining related to public security and the most recurrent techniques and technologies reported between 2014 to 2021, supporting outlining three axes for future directions: one with possible expansion of objectives for new research; another on changes and adaptations in scopes for the methodological context; and the last one on expansions and changes in application scenarios based on the literature.

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“…DT, SVM, ANN, k-NN, etc. [43,120,129,136] Misclassification costs, exceptionally putting more focus on the minority class.…”

Section: Thresholdmentioning

confidence: 99%

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Kuizinienė,

Krilavičius

2024

Electronics

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Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises.

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Large-scale machine learning for business sector prediction

Cited by 7 publications

References 19 publications

Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

Enabling Public Security Text-Based Analytics: A Survey to Outline Research Directions

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

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