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Constraint-aware Learning of Probabilistic Sequential Models for Multi-Label Classification

Created by
  • Haebom

Author

Mykhailo Buleshnyi, Anna Polova, Zsolt Zombori, Michael Benedikt

Outline

This paper addresses the multi-label classification problem involving a large number of labels. In particular, we consider the case where the output labels satisfy certain logical constraints. We propose an architecture that inputs classifiers for individual labels into an expressive sequential model to generate a joint distribution. One of the advantages of such an expressive model is its ability to model correlations that may arise from constraints. We experimentally demonstrate that the proposed architecture can exploit constraints during learning and enforce constraints at inference time.

Takeaways, Limitations

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Takeaways:
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We present a novel architecture that can effectively exploit logical constraints between labels in multi-label classification problems.
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We demonstrate that modeling correlations between labels can improve classification performance using expressive sequential models.
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We present a method to effectively apply constraints both at the learning and inference stages.
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Limitations:
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The performance of the proposed architecture may depend on certain types of constraints or datasets.
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Further research is needed on scalability to very large numbers of labels or complex constraints.
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Further analysis of the generalization performance of the proposed architecture is needed.
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