Estimating probability of banking crises using random forest

Hartini, Sri; Rustam, Zuherman; Saragih, Glori Stephani; Vargas, María Jesús Segovia

doi:10.11591/ijai.v10.i2.pp407-413

Cited by 9 publications

(11 citation statements)

References 13 publications

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“…The selection of these methods is based on their good performance in solving classification problems (Ariza-Garzón et al, 2021;Campillo et al, 2018). In fact, all these approaches have also been tested previously in assessing the performance of credit firms (Ariza-Garzón et al, 2020;Erdal & Karahanoglu, 2016;Golbayani et al, 2020;Hartini et al, 2021;Uddin et al, 2022). Due to the well-known nature of these methods, their detailed descriptions are not provided.…”

Section: Machine Learning Techniques and Their Applicationsmentioning

confidence: 99%

Sustainable finance: The role of savings and credit cooperatives in Ecuador

Segovia‐Vargas,

Miranda‐García,

Oquendo‐Torres

2023

Ann Public Coop Econ

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The current world situation leads us to consider that sustainable development needs to be a global priority to ensure the future of the planet and improve the quality of life. There is a need for sustainable finance to support this. Savings and credit cooperatives could help to achieve this impact as they serve the microfinance and microlending market. They facilitate the financial inclusion of the most vulnerable people, most of whom live in rural areas and are members of organizations, such as agricultural cooperatives and associations. Previous studies have focused exclusively on overall profitability, so this paper contributes to extending the literature by analyzing the whole population of savings and credit cooperatives in Ecuador (510 institutions), focusing on their profitability in two ways: the overall profitability necessary for the viability of the business and, in addition, the microcredit portfolio profitability, as a specific measure of its contribution to sustainability and social value creation. Another novelty is that the analysis has been carried out using several machine learning techniques for the wider generalization of the results. These show that size is the most relevant variable for predicting the ROE and that the microcredit portfolio profitability is conditioned by the credit variables.

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Section: Machine Learning Techniques and Their Applicationsmentioning

confidence: 99%

Sustainable finance: The role of savings and credit cooperatives in Ecuador

Segovia‐Vargas,

Miranda‐García,

Oquendo‐Torres

2023

Ann Public Coop Econ

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“…is the prediction of the class of the b th random forest (Hartini et al, 2021). The voting capability of RF reduces the noise and improves the robustness and prediction capability of the input data (Zhou et al, 2020).…”

Section: Random Forest Algorithm Uses Bootstrap Aggregation Technique...mentioning

confidence: 99%

Point and pixel inclusive machine learning models for exploring gully erosion susceptibility

Pal

Debanshi

Paul³

2022

Geocarto International

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Sample point based spatial model derived from Machine Learning (ML) algorithms often generalizes the spatial pattern of event. The present study has tried to highlight how far it is acceptable and can it replace pixel based modelling? The present study has presented a comparative view of pixel and sample point based modelling of gully erosion susceptibility of the upper Mayurakshi basin to assess the predictabilities. Random forest (RF), Support Vector Machine (SVM) and (ADB) were applied for developing pixel and point based models in WEKA and Python software environments respectively. From the models, it is found that 14-25% area located mainly in the upper parts of the study unit is very highly susceptible to gully erosion. Based on the accuracy and performance level using Area under curve (AUC) of Receiver operating curve, sensitivity, precision, F1 score, MCC pixel based ensemble models are superior to point based modelling. Often, the point based models have a very poor agreement with training and testing data. So, pixel based models could not be replaced with point based models. RF is found as the best representative model. The study, therefore, recommends using pixel based modelling for this or a similar purpose. Since the models have figured out the gully erosion susceptible areas, it would be a useful tool for related planning processes.

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“…Furthermore, machine learning algorithms can compare their calculated and accurate outputs to find errors in which the model can be modified accordingly [19]- [22]. One of the most classical machine learning techniques utilised for prediction is the random forest [23]- [25]. This technique is marked by being more flexible and straightforward to predict [26], as the forest consists of trees, and it is said that the more trees, the more influential the forest.…”

Section: Introductionmentioning

confidence: 99%

Null-values imputation using different modification random forest algorithm

Mijwil

Abdulqader²,

Ali³

et al. 2023

IJ-AI

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<p><span lang="EN-US">Today, the world lives in the era of information and data. Therefore, it has become vital to collect and keep them in a database to perform a set of processes and obtain essential details. The null value problem will appear through these processes, which significantly influences the behaviour of processes such as analysis and prediction and gives inaccurate outcomes. In this concern, the authors decide to utilise the random forest technique by modifying it to calculate the null values from datasets got from the University of California Irvine (UCL) machine learning repository. The database of this scenario consists of connectionist bench, phishing websites, breast cancer, ionosphere, and COVID-19. The modified random forest algorithm is based on three matters and three number of null values. The samples chosen are founded on the proposed less redundancy bootstrap. Each tree has distinctive features depending on hybrid features selection. The final effect is considered based on ranked voting for classification. This scenario found that the modified random forest algorithm executed more suitable accuracy results than the traditional algorithm as it relied on four parameters and got sufficient accuracy in imputing the null value, which is grown by 9.5%, 6.5%, and 5.25% of one, two and three null values in the same row of datasets, respectively.</span></p>

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Estimating probability of banking crises using random forest

Cited by 9 publications

References 13 publications

Sustainable finance: The role of savings and credit cooperatives in Ecuador

Sustainable finance: The role of savings and credit cooperatives in Ecuador

Point and pixel inclusive machine learning models for exploring gully erosion susceptibility

Null-values imputation using different modification random forest algorithm

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