Adaboost-LLP: A Boosting Method for Learning With Label Proportions
Adaboost-LLP: A Boosting Method for Learning With Label Proportions
复制标题
Adaboost-LLP：一种利用标签比例进行学习的增强方法
DOI：
10.1109/tnnls.2017.2727065
复制
发表时间：
2018-08-01
影响因子：
10.4
通讯作者：
Zhang, Peng
中科院分区：
文献类型：
--
作者：
Qi, Zhiquan;Meng, Fan;Zhang, Peng
如何解决仅使用标签比例的分类问题近年来在机器学习领域引起了越来越多的关注。在本文中，我们提出了一种集成学习策略来处理标签比例（LLP）的学习问题。具体地说，我们首先给出了LLP的基于不同权重的损失函数，然后构造了相应的弱分类器，同时利用标准的Logistic函数估计了其条件概率。最后，通过引入极大似然估计，我们提出了一种新的线性预测问题的anyboost学习系统（Adaboost-LLP）。与传统方法不同，该方法对训练集不做任何限制性假设;同时，与alter- $\propto $ SVM相比，Adaboost-LLP利用了更多额外的权值信息，并使用多个可有效求解的弱分类器来联合收割机组合成一个强分类器。实验结果表明，该方法在准确率和训练时间上均优于现有方法。
How to solve the classification problem with only label proportions has recently drawn increasing attention in the machine learning field. In this paper, we propose an ensemble learning strategy to deal with the learning problem with label proportions (LLP). In detail, we first give a loss function based on different weights for LLP, and then construct the corresponding weak classifier, at the same time, estimate its conditional probabilities by a standard logistic function. At last, by introducing the maximum likelihood estimation, we propose a new anyboost learning system for LLP (called Adaboost-LLP). Unlike traditional methods, our method does not make any restrictive assumptions on training set; at the same time, compared with alter- $\propto $ SVM, Adaboost-LLP exploits more extra weight information and uses multiple weak classifiers that can be solved efficiently to combine a strong classifier. All experiments show that our method outperforms the existing methods in both accuracy and training time.