基于两阶段随机森林的螺丝锁附结果判别研究  

Discriminant Research on Screw Locking Results Based on Two-stage Random Forest

作　　者：邓煜 李明 周稻祥 DENG Yu;LI Ming;ZHOU Daoxiang(College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China)

机构地区：[1]太原理工大学大数据学院

出　　处：《太原理工大学学报》2020年第2期198-205,共8页Journal of Taiyuan University of Technology

基　　金：国家自然科学基金资助项目(11771321);山西省社会发展科技攻关计划项目(201703D321032)

摘　　要：螺丝锁附结果的判断是智能螺丝机的核心要点。为更好地判别螺丝锁附结果,针对螺丝锁附数据不等长、类别不平衡的特点以及相似锁附类别易发生误判的问题,将随机森林分两阶段对螺丝锁附数据建立判别模型。第一阶段,根据原始数据的物理特性构造特征,对数据欠采样并使用随机森林算法进行特征筛选。第二阶段,首先以各物理特性的概率主成分分析方差作为特征进行聚类,将相似类别归在同簇中;然后对各簇分别使用随机森林算法建立分类模型。最终以先确定数据所属簇,再由簇内分类器分类的方式对螺丝锁附结果进行判别。实验结果显示,与传统螺丝锁附判别方法及经典机器学习分类算法对比,本文模型具有更优的精确度、召回率、F值。The judgment of screw locking results is the key point of intelligent screw locking machine. In order to better distinguish the results of screw locking, aimed at the characteristics of unequal length and category imbalance of screw locking data, and the problem of misjudgment of similar categories, a discrimination model was established for the screw locking data in two-stages based on random forest. In the first stage, the characteristics are constructed according to the physical characteristics of the original data. Moreover, the data is under-sampling and the random forest algorithm is used for feature selection. In the second stage, first, the probabilistic principal component analysis variance of each physical property is used as the feature to cluster, the similar categories are grouped in the same cluster, and then the random forest algorithm is used on each cluster to establish a classification model. Finally, the result of screw locking is distinguished by deter mining the cluster of data first and then classifying by classifier in the cluster. The experimental results show that compared with traditional screw locking method and classic machine learning classification algorithms, the model has better accuracy, recall rate and F value.

关 键 词：随机森林 类别不平衡 概率主成分分析 螺丝锁附 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]