mlsanityData to 14 Oct 2026

Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research

arXiv 2403.14483 ↗First posted 21 Mar 2024cs.LG cs.AI q-fin.STcited by 12

Not on the map. Why?

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.

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

3 of 16 references.

References · 3

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2024Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemblecited by 8
  3. 2024Enhancing Credit Card Fraud Detection A Neural Network and SMOTE Integrated Approachcited by 5

Built on by

Cited by year

Citing papers per year: 2024: 12.

12 papers.

  1. 2024Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) datacited by 26
  2. 2024A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Modelcited by 12
  3. 2024Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatmentcited by 11
  4. 2024Advanced Payment Security System:XGBoost, LightGBM and SMOTE Integratedcited by 8
  5. 2024Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Modelcited by 8
  6. 2024Integration of Mamba and Transformer -- MAT for Long-Short Range Time Series Forecasting with Application to Weather Dynamicscited by 7
  7. 2024Deep Reinforcement Learning for Mobile Robot Path Planningcited by 5
  8. 2024Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithmcited by 5
  9. 2024Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalizationcited by 5
  10. 2024Enhanced Credit Score Prediction Using Ensemble Deep Learning Modelcited by 2
  11. 2024Enhancing Exchange Rate Forecasting with Explainable Deep Learning Modelscited by 2
  12. 2024Multi-modal clothing recommendation model based on large model and VAE enhancementcited by 1