计算机与数字工程2023，Vol.51Issue(10)：2379-2383,5.DOI:10.3969/j.issn.1672-9722.2023.10.031
融合语义特征和统计特征的虚假招聘检测模型
A Recruitment Fraud Detection Method Combining Semantic Features and Statistical Features
摘要
Abstract
Aiming at the problem that current features cannot represent the semantics of recruitment information,this paper proposes a recruitment fraud detection method that combines semantic features and statistical features.This method firstly extracts the statistical features of recruitment information based on the analysis of recruitment behavior,secondly uses CNN to extract the se-mantic features of the recruitment information,then uses the fully connected neural network for feature fusion,and finally inputs the fused features into the cascade forest for classification.Comparing this method with other machine learning methods,the F1 value and F2 value are significantly improved.The experimental results confirm the feasibility of this method for false recruitment informa-tion detection.关键词
CNN/级联森林/虚假招聘Key words
CNN/cascade forest/recruitment fraud分类
军事科技引用本文复制引用
谢宁宁,杨新凯..融合语义特征和统计特征的虚假招聘检测模型[J].计算机与数字工程,2023,51(10):2379-2383,5.
