Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research

ArXiv ID: 2403.14483 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: credit assessment, LightGBM, ensemble learning, mobile internet, risk management

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders
• Why: The paper focuses on practical machine learning implementation (LightGBM, ensemble methods) with moderate mathematical depth, but demonstrates strong empirical rigor through its use of massive real-world operator data, detailed data preprocessing, feature engineering, and comparison of multiple modeling techniques.

  flowchart TD
    A["Research Goal: <br/>Mobile Internet User Credit Assessment <br/>for Communication Operators"] --> B["Data & Inputs: <br/>Operator-Sourced Big Data"]
    B --> C["Key Methodology: <br/>Preprocessing & Feature Engineering"]
    C --> D{"Computational Process"}
    D --> E["Base Model Construction<br/>Linear Regression, Decision Tree"]
    D --> F["Advanced Algorithm<br/>LightGBM"]
    E --> G["Ensemble Learning Integration<br/>Averaging, Voting, Blending, Stacking"]
    F --> G
    G --> H["Key Outcomes: <br/>Optimized Fusion Model & <br/>Improved Credit Evaluation Strategy"]