Comparison of Bankruptcy Prediction Models Using Support Vector Machine and Artificial Neural Network

Sulistiani, Ita; Widodo, Widodo; Nugraheni, Murien

doi:10.1109/eeccis54468.2022.9902935

2022 11th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS) 2022

DOI: 10.1109/eeccis54468.2022.9902935

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Comparison of Bankruptcy Prediction Models Using Support Vector Machine and Artificial Neural Network

Ita Sulistiani

Widodo Widodo

Murien Nugraheni

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2024

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Cited by 2 publications

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“…(a) ANN (artificial neural network) is a computational model interconnected with a layered structure that contains input, output, and one or more hidden layer [200]. The multi-layer perception (MLP) is a popular type of ANN, where a feed-forward manner is used to place nodes and layers.…”

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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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Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Kuizinienė,

Krilavičius

2024

Electronics

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A comparative study of feature selection and feature extraction methods for financial distress identification

Kuizinienė,

Savickas,

Kunickaitė

et al. 2024

PeerJ Computer Science

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Financial distress identification remains an essential topic in the scientific literature due to its importance for society and the economy. The advancements in information technology and the escalating volume of stored data have led to the emergence of financial distress that transcends the realm of financial statements and its’ indicators (ratios). The feature space could be expanded by incorporating new perspectives on feature data categories such as macroeconomics, sectors, social, board, management, judicial incident, etc. However, the increased dimensionality results in sparse data and overfitted models. This study proposes a new approach for efficient financial distress classification assessment by combining dimensionality reduction and machine learning techniques. The proposed framework aims to identify a subset of features leading to the minimization of the loss function describing the financial distress in an enterprise. During the study, 15 dimensionality reduction techniques with different numbers of features and 17 machine-learning models were compared. Overall, 1,432 experiments were performed using Lithuanian enterprise data covering the period from 2015 to 2022. Results revealed that the artificial neural network (ANN) model with 30 ranked features identified using the Random Forest mean decreasing Gini (RF_MDG) feature selection technique provided the highest AUC score. Moreover, this study has introduced a novel approach for feature extraction, which could improve financial distress classification models.

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Comparison of Bankruptcy Prediction Models Using Support Vector Machine and Artificial Neural Network

Cited by 2 publications

References 11 publications

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

A comparative study of feature selection and feature extraction methods for financial distress identification

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