A Novel Multi-Stage Stacked Ensemble Classifier using Heterogeneous Base Learners

A Novel Multi-Stage Stacked Ensemble Classifier using Heterogeneous Base Learners
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© 2023 by IJETT Journal
Volume-71 Issue-4
Year of Publication : 2023
Author : Pavitha N, Shounak Sugave
DOI : 10.14445/22315381/IJETT-V71I4P206

How to Cite?

Pavitha N, Shounak Sugave , "A Novel Multi-Stage Stacked Ensemble Classifier using Heterogeneous Base Learners," International Journal of Engineering Trends and Technology, vol. 71, no. 4, pp. 65-71, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I4P206

Abstract
The assessment of credit risk is essential to contemporary economies. Historically, statistical techniques and manual auditing have been used to measure credit risk associated with credit grants. Recent developments in financial AI are the result of a new generation of machine learning (ML)-driven credit risk models that have drawn a lot of interest from both business and academia. In this research, we aimed to improve ML algorithms' performance by optimizing hyperparameters. Also, we proposed a novel multi-stage heterogeneous stacked ensemble ML algorithm for predicting credit risk. Experimental results show a significant improvement in the performance of the proposed algorithm as compared to other hyperparameter-optimized ML algorithms. Two real-time data sets from emerging market economies are used to evaluate our model. Different evaluation metrics, namely precision, recall, f1-score and accuracy, are used for evaluating model performance.

Keywords
Machine Learning, Ensemble algorithm, Heterogeneous ensemble, Statistical modelling, Credit risk.

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