|Table of Contents|

Research on Drug Addicts Screening ModelBased on Random Forest Algorithm(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年02期
Page:
44-49
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
Research on Drug Addicts Screening ModelBased on Random Forest Algorithm
Author(s):
Gu Haiyan1; Wang Quan2
(1.Department of Computer Information and Cyber Security,Jiangsu Police Institute,Nanjing 210031,China)(2.Shenzhen Qianhai General Health Technology Co.,Ltd,Shenzhen 518000,China)
Keywords:
data preprocessing; Random Forest; classification model; recall rate; F1 score
PACS:
TP183
DOI:
10.3969/j.issn.1001-4616.2019.02.007
Abstract:
Based on data mining technology,using pulse wave data to construct screening model to detect drug addicts is a new technology research. After the pre-processing of pulse wave data,the original Random Forest classification model is initially established with high accuracy,but with a relatively low recall rate and F1 score. To resolve this issue,an improved classification model is henceforth proposed. The improved model mainly involves three improvement strategies:firstly,perform cross-validation by dividing multiple training sets and test sets to obtain generalization errors; Secondly,balance the sample distribution using down-sampling techniques; And finally,select model parameters based on multi-criteria analysis. According to the evaluation results of accuracy,precision,recall rates,and F1 scores,the performance of the improved Random Forest classification model has been significantly improved.

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Last Update: 2019-06-30