IEEE Trans Cybern. 2022 May;52(5):3658-3668. doi: 10.1109/TCYB.2020.3016048. Epub 2022 May 19.
Ensemble learning has many successful applications because of its effectiveness in boosting the predictive performance of classification models. In this article, we propose a semisupervised multiple choice learning (SemiMCL) approach to jointly train a network ensemble on partially labeled data. Our model mainly focuses on improving a labeled data assignment among the constituent networks and exploiting unlabeled data to capture domain-specific information, such that semisupervised classification can be effectively facilitated. Different from conventional multiple choice learning models, the constituent networks learn multiple tasks in the training process. Specifically, an auxiliary reconstruction task is included to learn domain-specific representation. For the purpose of performing implicit labeling on reliable unlabeled samples, we adopt a negative l -norm regularization when minimizing the conditional entropy with respect to the posterior probability distribution. Extensive experiments on multiple real-world datasets are conducted to verify the effectiveness and superiority of the proposed SemiMCL model.
集成学习因其在提高分类模型预测性能方面的有效性而有许多成功的应用。在本文中，我们提出了一种半监督多项选择学习（SemiMCL）方法，以便在部分标记数据上联合训练网络集成。我们的模型主要侧重于改进组成网络之间的标记数据分配，并利用未标记数据来捕获特定于域的信息，从而有效地促进半监督分类。与传统的多项选择学习模型不同，组成网络在训练过程中学习多个任务。具体来说，包括辅助重建任务以学习特定于域的表示。为了对可靠的未标记样本进行隐式标记，我们在最小化条件熵时采用负 l-范数正则化相对于后验概率分布。在多个真实数据集上进行了广泛的实验，以验证所提出的 SemiMCL 模型的有效性和优越性。
