Semi-supervised Classification Based on Abductive Learning

In scenarios where data is difficult to annotate and label noise is prevalent, traditional supervised learning methods are prone to performance degradation due to incomplete and noisy labels, which limits their classification capabilities. To address these challenges, we propose a semi-supervised classification framework based on abductive learning. By integrating knowledge graph reasoning with deep neural network modeling, the framework enhances the model’s ability to learn effectively in scenarios characterized by sparse annotations and noisy labels. The abductive learning framework generates high-quality label hypotheses through abductive reasoning, which are used both to complete unlabeled data and to correct erroneous annotations in the existing dataset, thereby improving the overall reliability of the training data. To prevent the model from over-relying on abductive labels, a hybrid loss strategy is proposed to balance and integrate supervision signals from both ground-truth annotations and abductive inferences. Experimental results demonstrate that the proposed method can effectively address issues such as label scarcity and noise, validating its capability to improve label quality and classification performance in semi-supervised learning scenarios.