LightGBM Model

LightGBM (Light Gradient Boosting Machine) is a gradient boosting framework used for efficient and accurate machine learning, primarily for classification and regression tasks. Current research focuses on enhancing LightGBM's robustness through techniques like topological data analysis for improved image classification and feature engineering for various applications, including credit risk assessment, medical diagnosis (e.g., sepsis, myocardial infarction, COVID-19), and wildlife re-identification. Its speed, scalability, and strong performance across diverse datasets make LightGBM a valuable tool with significant impact across numerous fields, from finance and healthcare to remote sensing and natural language processing.

Papers

October 14, 2024

MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media
Wei Zhai, Nan Bai, Qing Zhao, Jianqiang Li, Fan Wang, Hongzhi Qi, Meng Jiang, Xiaoqin Wang, Bing Xiang Yang, Guanghui Fu
Language Model General Analysis Mental Health Psychological Phenomenon LightGBM Model Weibo Dataset

August 7, 2024

Advanced User Credit Risk Prediction Model using LightGBM, XGBoost and Tabnet with SMOTEENN
Chang Yu, Yixin Jin, Qianwen Xing, Ye Zhang, Shaobo Guo, Shuchen Meng
Machine Learning Model High Dimensional Deep Model Risk Prediction XGBoost Model Credit Risk LightGBM Model SMOTE Integrated

July 1, 2024

Small Aerial Target Detection for Airborne Infrared Detection Systems using LightGBM and Trajectory Constraints
Xiaoliang Sun, Liangchao Guo, Wenlong Zhang, Zi Wang, Qifeng Yu
LightGBM Model Trajectory Design Small Aerial Target Detection

June 19, 2024

LightGBM robust optimization algorithm based on topological data analysis
Han Yang, Guangjun Qin, Ziyuan Liu, Yongqing Hu, Qinglong Dai
Image Classification Robust Optimization Topological Data Analysis Topological Feature LightGBM Model

May 17, 2024

Enhancing Understanding Through Wildlife Re-Identification
J. Buitenhuis
Human Understanding Metric Learning LightGBM Model Animal Re Identification

April 23, 2024

Explainable LightGBM Approach for Predicting Myocardial Infarction Mortality
Ana Letícia Garcez Vicente, Roseval Donisete Malaquias Junior, Roseli A. F. Romero
Machine Learning Risk Prediction Imputation Method LightGBM Model

March 21, 2024

Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research
Shaojie Li, Xinqi Dong, Danqing Ma, Bo Dang, Hengyi Zang, Yulu Gong
Credit Scoring User Perception LightGBM Model

February 28, 2024

Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble
Mengran Zhu, Ye Zhang, Yulu Gong, Kaijuan Xing, Xu Yan, Jintong Song
Ensemble Method XGBoost Model LightGBM Model Local Enhancement Default Prediction

February 27, 2024

Consistency Matters: Explore LLMs Consistency From a Black-Box Perspective
Fufangchen Zhao, Guoqiang Jin, Jiaheng Huang, Rui Zhao, Fei Tan
Black Box Strong Consistency Open Source LLM LLM Alignment LightGBM Model NLG Metric

February 2, 2024

Predicting ATP binding sites in protein sequences using Deep Learning and Natural Language Processing
Shreyas V, Swati Agarwal
Deep Learning Natural Language Processing BERT Model Protein Sequence LightGBM Model Multiple Embeddings Multi Site Adenosine Triphosphate

January 4, 2024

Shayona@SMM4H23: COVID-19 Self diagnosis classification using BERT and LightGBM models
Rushi Chavda, Darshan Makwana, Vraj Patel, Anupam Shukla
Ticket BERT Related Task New Task Binary Classification LightGBM Model English Tweet

December 2, 2023

From Beginner to Expert: Modeling Medical Knowledge into General LLMs
Qiang Li, Xiaoyan Yang, Haowen Wang, Qin Wang, Lei Liu, Junjie Wang, Yang Zhang, Mingyuan Chu, Sen Hu, Yicheng Chen, Yue Shen, Cong Fan, Wangshu Zhang, Teng Xu, Jinjie Gu, Jing Zheng, Guannan Zhang Ant Group
Large Language Model Expert Knowledge Pre Trained Large Language Model Medical Text Knowledge Injection LightGBM Model Medical Reasoning Novice Classification General Purpose LLM

November 7, 2023

Extending Machine Learning-Based Early Sepsis Detection to Different Demographics
Surajsinh Parmar, Tao Shan, San Lee, Yonghwan Kim, Jang Yong Kim
Machine Learning New Machine Ensemble Learning LightGBM Model Sepsis Detection

October 28, 2023

End-to-end Feature Selection Approach for Learning Skinny Trees
Shibal Ibrahim, Kayhan Behdin, Rahul Mazumder
Feature Selection Ensemble Learning Tree Ensemble LightGBM Model Joint Feature Interpretable Tree Differentiable Tree

October 5, 2023

Lightweight Boosting Models for User Response Prediction Using Adversarial Validation
Hyeonwoo Kim, Wonsung Lee
User Response LightGBM Model RecSys Challenge Adversarial Validation

September 30, 2023

Unveiling the Unborn: Advancing Fetal Health Classification through Machine Learning
Sujith K Mandala
Machine Learning LightGBM Model Fetal Health Fetal Heart

August 17, 2023

Efficient Commercial Bank Customer Credit Risk Assessment Based on LightGBM and Feature Engineering
Yanjie Sun, Zhike Gong, Quan Shi, Lin Chen
Feature Engineering Credit Risk LightGBM Model Multiple Classifier Customer Behavior

July 15, 2023

Enhanced Gradient Boosting for Zero-Inflated Insurance Claims and Comparative Analysis of CatBoost, XGBoost, and LightGBM
Banghee So
XGBoost Model LightGBM Model Extreme Gradient Boosting Insurance Data Actuarial Model Zero Inflated Catboost Model Claim Frequency

May 26, 2023

LightGBM Model

Papers

MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media

Advanced User Credit Risk Prediction Model using LightGBM, XGBoost and Tabnet with SMOTEENN

Small Aerial Target Detection for Airborne Infrared Detection Systems using LightGBM and Trajectory Constraints

LightGBM robust optimization algorithm based on topological data analysis

Enhancing Understanding Through Wildlife Re-Identification

Explainable LightGBM Approach for Predicting Myocardial Infarction Mortality

Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research

Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble

Consistency Matters: Explore LLMs Consistency From a Black-Box Perspective

Predicting ATP binding sites in protein sequences using Deep Learning and Natural Language Processing

Shayona@SMM4H23: COVID-19 Self diagnosis classification using BERT and LightGBM models

From Beginner to Expert: Modeling Medical Knowledge into General LLMs

Extending Machine Learning-Based Early Sepsis Detection to Different Demographics

End-to-end Feature Selection Approach for Learning Skinny Trees

Lightweight Boosting Models for User Response Prediction Using Adversarial Validation

Unveiling the Unborn: Advancing Fetal Health Classification through Machine Learning

Efficient Commercial Bank Customer Credit Risk Assessment Based on LightGBM and Feature Engineering

Enhanced Gradient Boosting for Zero-Inflated Insurance Claims and Comparative Analysis of CatBoost, XGBoost, and LightGBM

Improved Sales Forecasting using Trend and Seasonality Decomposition with LightGBM

Benchmarking state-of-the-art gradient boosting algorithms for classification