Enterprises have vast amounts of customer behavior data in the era of big data. How to take advantage of these data to evaluate custom forfeit risks effectively is a common issue faced by enterprises. Most of traditio...Enterprises have vast amounts of customer behavior data in the era of big data. How to take advantage of these data to evaluate custom forfeit risks effectively is a common issue faced by enterprises. Most of traditional customer churn predicting models ignore customer segmentation and misclassification cost, which reduces the rationality of model. Dealing with these deficiencies, we established a research model of customer churn based on customer segmentation and misclassification cost. We utilized this model to analyze customer behavior data of a telecom company. The results show that this model is better than those models without customer segmentation and misclassification cost in terms of the performance, accuracy and coverage of model.展开更多
当年龄识别被看做分类问题时,基于卷积神经网络(CNN)的方法通常直接采用一般图像分类的CNN进行年龄识别,常常忽略了进行人脸年龄识别时需要考虑的误分类代价问题。基于上述观察,提出一种基于代价敏感卷积神经网络(CS-CNN)的人脸年龄估...当年龄识别被看做分类问题时,基于卷积神经网络(CNN)的方法通常直接采用一般图像分类的CNN进行年龄识别,常常忽略了进行人脸年龄识别时需要考虑的误分类代价问题。基于上述观察,提出一种基于代价敏感卷积神经网络(CS-CNN)的人脸年龄估计方法。具体来讲,基于期望类最大原则(desired class maximum principle,DCMP)提出了一种能够使CNN学习到鲁棒人脸特征的代价敏感交叉熵损失函数(CS-CE),最后通过理论与实验的方法进行验证。相较之前的人脸年龄识别方法,该算法提升的效果是显著的。展开更多
文摘Enterprises have vast amounts of customer behavior data in the era of big data. How to take advantage of these data to evaluate custom forfeit risks effectively is a common issue faced by enterprises. Most of traditional customer churn predicting models ignore customer segmentation and misclassification cost, which reduces the rationality of model. Dealing with these deficiencies, we established a research model of customer churn based on customer segmentation and misclassification cost. We utilized this model to analyze customer behavior data of a telecom company. The results show that this model is better than those models without customer segmentation and misclassification cost in terms of the performance, accuracy and coverage of model.
文摘当年龄识别被看做分类问题时,基于卷积神经网络(CNN)的方法通常直接采用一般图像分类的CNN进行年龄识别,常常忽略了进行人脸年龄识别时需要考虑的误分类代价问题。基于上述观察,提出一种基于代价敏感卷积神经网络(CS-CNN)的人脸年龄估计方法。具体来讲,基于期望类最大原则(desired class maximum principle,DCMP)提出了一种能够使CNN学习到鲁棒人脸特征的代价敏感交叉熵损失函数(CS-CE),最后通过理论与实验的方法进行验证。相较之前的人脸年龄识别方法,该算法提升的效果是显著的。
