Somatotype identification of middle-aged women based on decision tree algorithm

Abstract

PurposeBody type classification has a great influence on plate making and garment sizing system, and the accuracy of body type classification method will greatly affect the fit of garment production. The purpose of this paper is to use the decision tree algorithm to study body classification rules, develop a decision tree body recognition model and judge the body shape of middle-aged women in Xinjiang.Design/methodology/approachFirst, perform dimensionless processing on the collected data of 256 middle-aged women in Xinjiang, and the dimensionless data were used for K-means body clustering; Then, quantitatively analyze the effectiveness of different classification clusters based on the silhouette coefficients. Second, the decision tree algorithm is used to divide the classified sample data into a training set and a test set at a ratio of 70/30, and select the best node and the best branch based on the Gini coefficient to construct a classification tree. Last, the overall optimal decision tree is generated by means of hyperparameter pruning.FindingsThe body shape of middle-aged women in Xinjiang can be divided into three types: standard body, plump body and obese body. The decision tree model has an excellent effect on body classification of middle-aged women in Xinjiang (precision (macro), 95.46%; precision (micro), 95.95%; recall (macro), 95.46%; recall (micro), 95.95%; F1 (macro), 95.46%; F1 (micro), 95.95%).Originality/valueFor scientific research, this paper is conducive to increasing the regional body type theory and stimulating the establishment of a garment sizing subdivision system in Xinjiang. In terms of production practice, this paper not only establishes a model for judging the shape of middle-aged women in Xinjiang, but also provides reference data for intermediates of various sizes. In addition, to facilitate pattern-making and the establishment of a subdivision system for the size of middle-aged women's garments in Xinjiang, this paper provides the grading values of various body control parts of middle-aged women in Xinjiang.

Discussion on the Enterprise Financial Risk Management Framework Based on AI Fintech

“…The results show that the algorithm provides good classification and increases security and accuracy in digital payments [7]. When Wang et al studied the conversion of credit funds, to consider risk management, a decision tree algorithm was introduced to evaluate it [6]. The experimental results show that the risk estimation accuracy of the decision tree algorithm reaches 81.2% and 83.6%, respectively, which proves that the model can be used as an effective reference for pre-lending risk assessment.…”

“…The results show that the effectiveness of the decision tree algorithm in classifying female body types. And the classification accuracy of various body types is above 95% [6]. When Ariyati et al studied electronic payment in financial technology, to improve the security and accuracy of electronic payment, a decision tree algorithm was introduced to analyze and process it.…”