Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 476-481.

• Big Date & Date Mining • Previous Articles     Next Articles

Improved XGBoostModel Based on Genetic Algorithm for Hypertension Recipe Recognition

LEI Xue-mei, XIE Yi-tong   

  1. School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China
  • Online:2018-06-20 Published:2018-08-03

Abstract: A novel improved XGBoost (eXtreme Gradient Boosting) model based on genetic algorithmfor hypertension recipe recognition was proposed.The model consists of three steps.Firstly,data pre-processing is employed to handle missing values,remove duplicate data and analyze data feature.Then,the genetic algorithm is used to optimize theparameters of XGBoost model adaptively.At last,hypertension recipe identification model is trained according to the optimal parameters.The results show that the parameters optimized by genetic algorithm performs better than grid search.Moreover,the proposed model outperforms other four models (Random forest,GBDT,Bagging and AdaBooster) over four evaluation measures:accuracy,recall rate,F1 and the area under the curve (AUC) on average,and enhances the interpretability of credit scoring model.

Key words: Data analysis, Genetic algorithm, Hypertension recipes, XGBoost

CLC Number: 

  • TP181
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