Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation

Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks

“…Recent research has developed a hybrid intelligent model to combine the advantages of individual models and avoid their weaknesses (Sánchez-Lasheras, de Andrés, Lorca & de Cos Juez, 2012;Tsai, Hsu & Yen, 2014;Xu, Xiao, Dang, Yang & Yang, 2014;Zhou, Lai & Yen, 2012). A technique is called hybrid if several soft computing approaches are applied in the analysis and only one predictor is used to make the final prediction, or outputs of several predictors are combined, to obtain an ensemble-based prediction.…”

Economic and financial determinants of bankrupcy: evidence from Ecuador's private banks and the impact of dollarization on financial fragility

“…Furthermore, in the technical aspect, the problem of having a small sample (approximately 26 banks on average in the last 20 years) and the existence of rare events, that is, binary dependent variables with tens of thousands of times fewer events (bankruptcy) than nonevents (no bankruptcy), is made evident and adequately addressed. Papers such as those by Yang et al (2011), Huang et al (2012), and Zhou et al (2014) address the issue of small sample size using the support vector machine (SVM) technique, confirming the special ability of this technique to perform well in terms of prediction using a small dataset. However, SVM models are quite complicated to understand because the coefficients that are assigned to the variables are difficult to interpret (Tseng, & Hu, 2010;Jeong et al, 2012;Alaka et al, 2018).…”

MultiClass Decision Forest Machine Learning Artificial Intelligence

“…Pembelajaran mesin telah terbukti menyelesaikan banyak tugas. Pembelajaran mesin untuk memprediksi sebelumnya telah digunakan dalam konteks memprediksi kebangkrutan dengan SVM [6] dan Robust Logistic Regression [7], model ini memiliki keterbatasan. Dimana nilai teknik ini sangat bergantung pada kemampuan peneliti untuk memasukkan variabel independen yang benar.…”