Open AccessEnsemble machine learning algorithm optimization of bankruptcy prediction of bank
Author(s) -
Bambang Siswoyo,
Zuraida Abal Abas,
Ahmad Naim Che Pee,
Rita Komalasari,
Nano Suryana
Publication year - 2022
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 7
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v11.i2.pp679-686
Subject(s) - computer science , random forest , ensemble learning , ensemble forecasting , machine learning , support vector machine , artificial intelligence , bootstrap aggregating , bankruptcy prediction , artificial neural network , set (abstract data type) , multiple models , data mining , algorithm , bankruptcy , finance , programming language , economics
The ensemble consists of a single set of individually trained models, the predictions of which are combined when classifying new cases, in building a good classification model requires the diversity of a single model. The algorithm, logistic regression, support vector machine, random forest, and neural network are single models as alternative sources of diversity information. Previous research has shown that ensembles are more accurate than single models. Single model and modified ensemble bagging model are some of the techniques we will study in this paper. We experimented with the banking industry’s financial ratios. The results of his observations are: First, an ensemble is always more accurate than a single model. Second, we observe that modified ensemble bagging models show improved classification model performance on balanced datasets, as they can adjust behavior and make them more suitable for relatively small datasets. The accuracy rate is 97% in the bagging ensemble learning model, an increase in the accuracy level of up to 16% compared to other models that use unbalanced datasets.