A Novel Feature Selection Based Ensemble Approach to Bankruptcy Detection

Abdullahi-Attah, Adebola (2020) A Novel Feature Selection Based Ensemble Approach to Bankruptcy Detection. Masters thesis, Dublin, National College of Ireland.

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Abstract

In domains such as finance, explainable machine learning approaches are important if they are to back decision making systems especially in detecting potential bankruptcy where there are a myriad of attributes such as net profit, liquidity ratio that varies with each institution. With the business expansion and increase in company data, to foster explainable machine learning processes and potentially improve performance, there is a need to identify the most important indicators from highly dimensional data that can enhance bankruptcy detection and enable company owners to investigate financial statements with less need for external audit. Hence, this research investigates an optimal selection of features to detect bankruptcy using an ensemble approach combining six feature selection techniques namely Pearson’s correlation, information gain, exhaustive feature selection, gradient boosting trees feature importance, random shuffling, and recursive feature elimination, through different voting mechanisms. For prediction, an ensemble approach is also employed combining random forest (RF), extreme gradient boosting (XGboost) and particle swarm optimized artificial neural network (PSO-ANN). The results indicate that ensemble approach for both feature selection and prediction outperformed state-of-the-art research in this domain with about 98% AUC score and 34 pertinent features were identified as major indicators of potential bankruptcy.
Keywords- bankruptcy, PSO-ANN, XGBoost, random forest, ensemble, financial statement.

Item Type:	Thesis (Masters)
Subjects:	H Social Sciences > HG Finance Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Dan English
Date Deposited:	18 Jan 2021 15:38
Last Modified:	18 Jan 2021 15:38
URI:	https://norma.ncirl.ie/id/eprint/4377

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