Self-Training of Halfspaces with Generalization Guarantees under Massart Mislabeling Noise Model

• URL: http://arxiv.org/abs/2111.14427v2
• Date: Thu, 2 Dec 2021 12:32:46 GMT
• Title: Self-Training of Halfspaces with Generalization Guarantees under Massart Mislabeling Noise Model
• Authors: Lies Hadjadj, Massih-Reza Amini, Sana Louhichi, Alexis Deschamps
• Abstract summary: We investigate the generalization properties of a self-training algorithm with halfspaces. The approach learns a list of halfspaces iteratively from labeled and unlabeled training data.
• Score: 5.4826939033861155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate the generalization properties of a self-training algorithm with halfspaces. The approach learns a list of halfspaces iteratively from labeled and unlabeled training data, in which each iteration consists of two steps: exploration and pruning. In the exploration phase, the halfspace is found sequentially by maximizing the unsigned-margin among unlabeled examples and then assigning pseudo-labels to those that have a distance higher than the current threshold. The pseudo-labeled examples are then added to the training set, and a new classifier is learned. This process is repeated until no more unlabeled examples remain for pseudo-labeling. In the pruning phase, pseudo-labeled samples that have a distance to the last halfspace greater than the associated unsigned-margin are then discarded. We prove that the misclassification error of the resulting sequence of classifiers is bounded and show that the resulting semi-supervised approach never degrades performance compared to the classifier learned using only the initial labeled training set. Experiments carried out on a variety of benchmarks demonstrate the efficiency of the proposed approach compared to state-of-the-art methods.

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