Search Results for author:
Found 7 papers, 0 papers with code
Improving Investment Suggestions for Peer-to-Peer (P2P) Lending via Integrating Credit Scoring into Profit Scoring
The studies have mainly focused on two categories to guide the lenders' investments: one aims at minimizing the risk of investment (i. e., the credit scoring perspective) while the other aims at maximizing the profit (i. e., the profit scoring perspective).
Developing and Improving Risk Models using Machine-learning Based Algorithms
The objective of this study is to develop a good risk model for classifying business delinquency by simultaneously exploring several machine learning based methods including regularization, hyper-parameter optimization, and model ensembling algorithms.
Predicting class-imbalanced business risk using resampling, regularization, and model ensembling algorithms
We aim at developing and improving the imbalanced business risk modeling via jointly using proper evaluation criteria, resampling, cross-validation, classifier regularization, and ensembling techniques.
Risk Prediction of Peer-to-Peer Lending Market by a LSTM Model with Macroeconomic Factor
Our study can broaden the applications of the LSTM algorithm by using it on the sequential P2P data and guide the investors in making investment strategies.
A XGBoost risk model via feature selection and Bayesian hyper-parameter optimization
TPE optimization shows a superiority over RS since it results in a significantly higher accuracy and a marginally higher AUC, recall and F1 score.
An Automatic Interaction Detection Hybrid Model for Bankcard Response Classification
In the first stage of the hybrid model, CHAID analysis is used to detect the possibly potential variable interactions.
A two-stage hybrid model by using artificial neural networks as feature construction algorithms
The hybrid model uses a very simple neural network structure as the new feature construction tool in the first stage, then the newly created features are used as the additional input variables in logistic regression in the second stage.
