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A Dynamic Masking-Based Multi-Learning Framework for Sparse Classification
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AI
DOI：10.32604/cmc.2025.069949.png)
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Abstract
En 中文
With the recent increase in data volume and diversity, traditional text representation techniques are struggling to capture context, particularly in environments with sparse data. To address these challenges, this study proposes a new model, the Masked Joint Representation Model (MJRM). MJRM approximates the original hypothesis by leveraging multiple elements in a limited context. It dynamically adapts to changes in characteristics based on data distribution through three main components. First, masking-based representation learning, termed selective dynamic masking, integrates topic modeling and sentiment clustering to generate and train multiple instances across different data subsets, whose predictions are then aggregated with optimized weights. This design alleviates sparsity, suppresses noise, and preserves contextual structures. Second, regularization-based improvements are applied. Third, techniques for addressing sparse data are used to perform final inference. As a result, MJRM improves performance by up to 4% compared to existing AI techniques. In our experiments, we analyzed the contribution of each factor, demonstrating that masking, dynamic learning, and aggregating multiple instances complement each other to improve performance. This demonstrates that a masking-based multi-learning strategy is effective for context-aware sparse text classification, and can be useful even in challenging situations such as data shortage or data distribution variations. We expect that the approach can be extended to diverse fields such as sentiment analysis, spam filtering, and domain-specific document classification.
Keywords:
Text classification
dynamic learning
contextual features
data sparsity
masking-based representation
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