摘要
利用主分量分类方法,研究改进的基于主分量分类的交通事件自动检测算法。主分量分类方法是一种改进的两类模型分类法。该分类法求解样本方向,该方向可以看作超平面的法方向,根据这个方向将样本中一类数据从另一类数据中分离。样本在法方向上的投影用来估计每个实例的条件概率,然后根据贝叶斯规则实现实例的分类。对于线性不可分等复杂的分类问题,可通过核函数作用将数据映射到高维特征空间中实现线性可分。最后对I-880高速公路事件数据的仿真结果表明,KPCC算法获得了100.00%的检测率、1.82%的误警率和1.02分钟的平均检测时间。
An improved automatic incident detection algorithm,based on principal component classifier,is constructed.Principal component classifier is an improved two models classification technology.This method computes a direction from a dataset which can be seen as the normal direction of a hyperlane such that samples in one class can be separated well from the other by this hyperlane.The projections onto that direction can be used for estimating class-conditional possibility density function（pdf） according to Bayes rule.The algorithm can also be carried out in the feature space to deal with more complicated classification problem by mapping input data into a high dimensional feature space using kernel function.Finally the simulation results on I-880 freeway dataset are：DR is 100.00%,FAR is 1.82%,and the MTTD is 1.02 minutes.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第1期245-248,共4页
Computer Engineering and Applications
基金
江苏省教委自然科学基金资助项目(No.08KJB580004)
关键词
交通工程
事件自动检测
主分量分类
核函数
贝叶斯规则
traffic engineering
Automatic Incident Detection（AID）
principal component classifier
kernel function
Bayes rule
