# Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research

arXiv: https://arxiv.org/abs/2403.14483 · HTML: https://mlsanity.com/paper/2403.14483/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble](https://mlsanity.com/paper/2402.17979/) (2024)
- [Enhancing Credit Card Fraud Detection A Neural Network and SMOTE Integrated Approach](https://mlsanity.com/paper/2405.00026/) (2024)

## Built on by (most cited first, 12 of 12)

- [Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data](https://mlsanity.com/paper/2407.00028/) (2024)
- [A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model](https://mlsanity.com/paper/2407.08942/) (2024)
- [Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment](https://mlsanity.com/paper/2404.12634/) (2024)
- [Advanced Payment Security System:XGBoost, LightGBM and SMOTE Integrated](https://mlsanity.com/paper/2406.04658/) (2024)
- [Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model](https://mlsanity.com/paper/2410.17545/) (2024)
- [Integration of Mamba and Transformer -- MAT for Long-Short Range Time Series Forecasting with Application to Weather Dynamics](https://mlsanity.com/paper/2409.08530/) (2024)
- [Deep Reinforcement Learning for Mobile Robot Path Planning](https://mlsanity.com/paper/2404.06974/) (2024)
- [Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithm](https://mlsanity.com/paper/2405.10762/) (2024)
- [Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization](https://mlsanity.com/paper/2409.16973/) (2024)
- [Enhanced Credit Score Prediction Using Ensemble Deep Learning Model](https://mlsanity.com/paper/2410.00256/) (2024)
- [Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models](https://mlsanity.com/paper/2410.19241/) (2024)
- [Multi-modal clothing recommendation model based on large model and VAE enhancement](https://mlsanity.com/paper/2410.02219/) (2024)

## Abstract (arXiv)

Mobile Internet user credit assessment is an important way for communication operators to establish decisions and formulate measures, and it is also a guarantee for operators to obtain expected benefits. However, credit evaluation methods have long been monopolized by financial industries such as banks and credit. As supporters and providers of platform network technology and network resources, communication operators are also builders and maintainers of communication networks. Internet data improves the user's credit evaluation strategy. This paper uses the massive data provided by communication operators to carry out research on the operator's user credit evaluation model based on the fusion LightGBM algorithm. First, for the massive data related to user evaluation provided by operators, key features are extracted by data preprocessing and feature engineering methods, and a multi-dimensional feature set with statistical significance is constructed; then, linear regression, decision tree, LightGBM, and other machine learning algorithms build multiple basic models to find the best basic model; finally, integrates Averaging, Voting, Blending, Stacking and other integrated algorithms to refine multiple fusion models, and finally establish the most suitable fusion model for operator user evaluation.