摘要
针对交通事件数据样本少,检测效率低的问题,将加权支持向量机引入到交通事件检测中,采用样本重要度加权法提高算法的检测率,根据识别误差确定样本重要度权值,建立了交通事件检测的样本重要度加权法支持向量机算法,最后应用实测数据对标准支持向量机算法、样本重要度加权法、样本数目加权法3种算法的检测效果进行测试。研究结果表明:样本数目加权法算法能够根据样本的好坏自适应确定样本重要度权值,提高了算法的鲁棒性;当负正样本比率减少时,3种算法的检测效果均变差,而对于同样的样本,标准支持向量机的检测率最低,样本重要度加权法的效果最好,加权算法的选择要依据样本的数量、分布不平衡以及识别目标而定;在交通事件检测中,为了提高检测率,选择样本重要度加权效果最好,在不同的样本不平衡率下,检测效果是不同的,不平衡率越严重,检测效果越差。
A weighed support vector machine（SVM）, based on the importance of the samples, was proposed to solve the problem of low detection caused by imablanced samples. The weight of the samples was determined by their discriminate errors. The measured data was used to test the performance of the proposed algorithm. The basic SVM, the weighed SVM based on the sample number and the weighed SVM based on sample importance were tested under different imbalanced samples. The results show that the algorithm can determine the weight of the sample according the sample, which can improve the robust of the algorithm; the bigger the imbalance of the sam ples, the lower the detection ratio of all three algorithms. With the same samples, the ratio of de tection of the basic SVM is the lowest and the weighed SVM based on sample importance is the highest;in traffic incident detecting, in order to improve detecting rates, the weighted SVM based on sample important is the best choice; under different unbalanced sample rates, detection effects are different; the higher the unbalanced rate, the worse the detection effect. 1 tab, 1 fig, 9 refs.
出处
《长安大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2013年第6期84-87,共4页
Journal of Chang’an University(Natural Science Edition)
基金
国家自然科学基金项目(51208053)
关键词
交通工程
交通事件检测
不平衡样本
加权支持向量机
样本重要度
traffic engineering
automatic incident detection
imbalanced sample
weighed supportvector machine~ importance
