Details

Autor(en) / Beteiligte

• Qi, Zhiquan
• Meng, Fan
• Tian, Yingjie
• Niu, Lingfeng
• Shi, Yong
• Zhang, Peng

Titel

Adaboost-LLP: A Boosting Method for Learning With Label Proportions

Ist Teil von

• IEEE transaction on neural networks and learning systems, 2018-08, Vol.29 (8), p.3548-3559

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2018

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• 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-<inline-formula> <tex-math notation="LaTeX">\propto </tex-math></inline-formula>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.