虚警率约束的联合弱分类器集成学习算法
发布时间：2018-12-14 16:15
【摘要】：提出一种联合弱分类器集成学习算法。借鉴Adaboost方法采用弱分类器构建强分类器的思想,联合多个弱分类器构建特征分类的得分函数,生成一个集成分类器。在分类器训练时,采用ROC曲线围成的AUC面积值构建目标函数,加入虚警率上下限约束条件,采用列生成算法学习弱分类器,采用割平面法学习弱分类器的系数。在PASCAL VOC-2007数据集上进行目标检测实验,实验结果表明,与常用的支持向量机、Adaboost、随机森林和卷积神经网络分类方法相比,该方法的假正率指标低,真正率指标高。
[Abstract]:A joint weak classifier ensemble learning algorithm is proposed. Using the idea of weak classifier to construct strong classifier using Adaboost method and combining several weak classifiers to construct the score function of feature classification, an integrated classifier is generated. In classifier training, the objective function is constructed by the AUC area value surrounded by ROC curve, the upper and lower bound constraints of false alarm rate are added, the weak classifier is learned by column generation algorithm, and the coefficient of weak classifier is studied by cutting plane method. The experimental results of target detection on PASCAL VOC-2007 data sets show that the false positive rate index is lower and the real rate index is higher than the usual classification methods such as support vector machine, Adaboost, stochastic forest and convolution neural network.
【作者单位】： 苏州信息职业技术学院计算机科学与技术系;郑州财经学院信息工程学院;太原理工大学电气工程学院;
【基金】：河南省科技厅科技计划课题基金项目(112102310550)
【分类号】：TP181
本文编号：2378929
[Abstract]:A joint weak classifier ensemble learning algorithm is proposed. Using the idea of weak classifier to construct strong classifier using Adaboost method and combining several weak classifiers to construct the score function of feature classification, an integrated classifier is generated. In classifier training, the objective function is constructed by the AUC area value surrounded by ROC curve, the upper and lower bound constraints of false alarm rate are added, the weak classifier is learned by column generation algorithm, and the coefficient of weak classifier is studied by cutting plane method. The experimental results of target detection on PASCAL VOC-2007 data sets show that the false positive rate index is lower and the real rate index is higher than the usual classification methods such as support vector machine, Adaboost, stochastic forest and convolution neural network.
【作者单位】： 苏州信息职业技术学院计算机科学与技术系;郑州财经学院信息工程学院;太原理工大学电气工程学院;
【基金】：河南省科技厅科技计划课题基金项目(112102310550)
【分类号】：TP181
【相似文献】
相关期刊论文 前3条
1 郭立娟;;非线性弱分类器的存在性[J];兰州理工大学学报;2012年02期
2 谢红跃;方昱春;蔡起运;;一种新的改进AdaBooat弱分类器训练算法[J];中国图象图形学报;2009年11期
3 赵梅芳;罗阿理;吴福朝;赵永恒;;自适应增强方法在光谱自动分类中的应用[J];光谱学与光谱分析;2008年02期
，本文编号：2378929
本文链接：https://www.wllwen.com/kejilunwen/zidonghuakongzhilunwen/2378929.html
