计算机与现代化Issue(9)：74-81,90,9.DOI:10.3969/j.issn.1006-2475.2024.09.013
基于Focal Loss改进LightGBM的供水管网毛刺数据检测
Water Supply Pipeline Burr Data Detection Based on Improved LightGBM by Focal Loss
摘要
Abstract
Addressing the issue of low recall in the detection of burrs in water supply pipelines due to data imbalance,this paper proposes an improved method for detecting pipeline burr data by utilizing the Focal Loss function and integrating it with Light-GBM.Firstly,considering the characteristics of pipeline burr data,neighborhood-related features are constructed.Secondly,the Focal Loss function is introduced into LightGBM to increase the model's weight on hard-to-detect burr samples.Different pa-rameter values for Focal Loss are experimented to balance precision and recall.Finally,different parameter settings for Focal Loss are selected for model fusion to further improve the detection performance of the model on imbalanced burr data.Experi-ments are carried out on a real dataset from a municipal water supply pipeline.The experimental results show that,compared with a single model based on the cross-entropy loss function,the fused model with the improved Focal Loss in this paper achieves 33.3 percentage points increase in recall and 18 percentage points increase in F1 score for burr data.However,the pre-cision of burr data detection still needs further improvement.The method proposed in this paper starts with loss function and dy-namically adjusts the weights of difficult and easy samples to effectively improve the detection performance of burr data under un-balanced data.关键词
异常检测/Focal Loss/LightGBM/不平衡数据/毛刺数据Key words
anomaly detection/Focal Loss/LightGBM/imbalanced data/burr data分类
计算机与自动化引用本文复制引用
薛浩,马静,郭小宇..基于Focal Loss改进LightGBM的供水管网毛刺数据检测[J].计算机与现代化,2024,(9):74-81,90,9.基金项目
国家自然科学基金面上项目(72174086) （72174086）
