基于XGBoost算法对新疆女性臀部体型判别及原型修正

为准确判断下体体型,使新疆地区女性裙装更加合体,分析了220名18~25岁的新疆地区青年女性臀部数据,通过因子分析与相关指数得到聚类指标:后臀长腰围比、后臀长臀围比,采用K-means聚类方法将臀部分为3类,使用Python软件建立XGBOOST臀部判别模型。首先对不同算法模型进行比较分析表明,运用XGBOOST方法的测试集精准度最高为98.4%。其次修正新疆地区中间体的裙装原型,发现其后臀长与标准裙装原型后臀长相差2.4 cm,说明新疆地区女性臀部相比国内其他地区偏翘。将判别算法运用到数据系统中,可提高体型判别效率,为人体相关领域提供数据支持。

Young Xinjiang female hip shape characterization and prototype correction using XGBoost algorithm

This paper addresses the fitting issues for Xinjiang skirts through a study on the size of lower body shape. In order to determine the true shape of hip body accurately, hip data of 220 young Xinjiang females aged 18 to 25 were analyzed. Using the factor analysis and correlation coefficients mergods, 2 factor for clustering were obtained, i.e. back hip length to waist girth ratio, back hip length to hip girth ratio, and 3 hip body types were defined based on the K-means clustering method. On this basis, Python software was used to establish an XGBoost buttock discriminant model. The work involved three parts. The first thing was to compare and analyze different algorithm models and XGBoost was used to evaluate the accuracy, and the highest accuracy reached 98.4%. The second part was to,modify the intermediate skirt prototype for young Xinjiang females, where it was found that the rear hip length was 2.4 cm different from the rear hip length of the standard dress prototype, indicating that the young Xingjiang female hips is larger than that in other regions. Finally, the discriminating algorithm was applied to the data system. The outcome of the research is shown to have improved discrimination efficiency for body size, and the method provides data support for other human related fields.