Unified Deep Ensemble Architecture for Multiple Classification Tasks

Mistry, Kody A. J.; Mandal, Bappaditya

Abstract

Banks face regular challenges in making decisions for ever increasing need for bank loans. Most banks use applicant’s financial situations, their past history, affordability checks, credit score and risk assessment, which are time consuming, challenging, tedious process and prone to errors. Although many existing machine learning algorithms are employed to extract crucial information involving pattern/behaviour of the loan applicants, significance challenges still exist. In this work, we propose a unified deep ensemble architecture for multiple classification tasks (DEAMT) solving problems that are diverse in nature with a focus on financial datasets for bank loan approval. Traditional machine learning algorithms focus on domain specific problems for classification task ignoring their generalisation capability across multiple domain applications. DEAMT is a novel architecture that uses concatenated decision trees and convolution neural networks configured in both sequential and parallel architectures for optimising multiple classification tasks across multiple domains. The proposed architecture is very versatile for both large and smaller datasets across multiple domains. Extensive experimental results, analysis and ablation studies on various diverse datasets handling various classification problems show the superiority of our proposed architecture as compared to the baseline and other popular emerging methods.

Citation

Mistry, K. A. J., & Mandal, B. (2024, August). Unified Deep Ensemble Architecture for Multiple Classification Tasks. Presented at 2024 Intelligent Systems Conference (IntelliSys), Amsterdam, The Netherlands

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 Intelligent Systems Conference (IntelliSys)
• Start Date: Aug 29, 2024
• End Date: Aug 30, 2024
• Acceptance Date: Jul 31, 2024
• Online Publication Date: Jul 31, 2024
• Publication Date: 2024
• Deposit Date: Dec 3, 2024
• Pages: 544-557
• Series Title: Lecture Notes in Networks and Systems
• Book Title: Intelligent Systems and Applications
• ISBN: 9783031663284; 9783031663291
• DOI: https://doi.org/10.1007/978-3-031-66329-1_35
• Public URL: https://keele-repository.worktribe.com/output/1011892
• Additional Information: First Online: 31 July 2024; Conference Acronym: IntelliSys; Conference Name: Intelligent Systems Conference; Conference City: Amsterdam; Conference Country: The Netherlands; Conference Year: 2024; Conference Start Date: 29 August 2024; Conference End Date: 30 August 2024; Conference ID: intellisys12024; Conference URL: http://saiconference.com/IntelliSys