A novel hybrid deep long short-term memory – multilayer perception model with feature fusion for loan risk prediction
學年 115
學期 1
出版（發表）日期 2026-11-05
作品名稱 A novel hybrid deep long short-term memory – multilayer perception model with feature fusion for loan risk prediction
作品名稱（其他語言）
著者 Tzu-Chia Chen
單位
出版者
著錄名稱、卷期、頁數 Journal of Statistical Computation and Simulation
摘要 Loan risk prediction is a crucial task for financial institutions to assess the likelihood of a borrower defaulting on their loan. This research presents a novel approach for loan risk prediction using a hybrid Deep Long Short-Term Memory (LSTM) - Multilayer Perception (MLP) architecture by feature fusion. Feature fusion is a crucial technique in loan prediction that involves combining multiple features or variables to create a new, more informative feature. The proposed architecture combines the strengths of LSTM and MLP models to leverage both the sequential dependencies of input features and the parallel processing capabilities of MLP. The LSTM model captures the temporal dynamics and dependencies within the loan data by employing Recurrent Neural Network (RNN). . The Hybrid deep LSTM-MLP acquired the maximum rate of accuracy of 97.78%, a rate of specificity of 93.92% and FNR of 0.026.
關鍵字 Prediction; loan risk; finance; feature fusion; autoencoder; deep LSTM; MLP
語言 en
ISSN
期刊性質 國內
收錄於 SCI Scopus
產學合作
通訊作者
審稿制度 否
國別 USA
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/130051 )