Hierarchy-preserving learning for multi-label classification

One or more computing devices, comprising:

• one or more non-transitory computer-readable storage media including instructions;
• and
• one or more processors coupled to the one or more storage media, the one or more processors configured to execute the instructions to:
• access a set of content objects, wherein each content object is pre-labeled with one or more concepts of a plurality of concepts, and wherein the plurality of concepts are organized according to a hierarchical relationship;
• and
• train, by a machine-learning model, a classification model for classifying content objects within the set of content objects, wherein training the classification model comprises:
• determine, for each object, a plurality of classification values corresponding to the plurality of concepts;
• calculate a loss for each of the plurality of classification values based on the pre-labeled concepts associated with the object;
• utilize a hierarchical constraint loss function to calculate a maximum loss based on the calculated loss for each of the plurality of classification values;
• and
• update the classification model based on the hierarchical constraint loss function until the maximum loss satisfies a predetermined criterion.

A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of one or more computing devices, cause the one or more processors to:

• access a set of content objects, wherein each content object is pre-labeled with one or more concepts of a plurality of concepts, and wherein the plurality of concepts are organized according to a hierarchical relationship;
• and
• train, by a machine-learning model, a classification model for classifying content objects within the set of content objects, wherein training the classification model comprises:
• determine, for each object, a plurality of classification values corresponding to the plurality of concepts;
• calculate a loss for each of the plurality of classification values based on the pre-labeled concepts associated with the object;
• utilize a hierarchical constraint loss function to calculate a maximum loss based on the calculated loss for each of the plurality of classification values;
• and
• update the classification model based on the hierarchical constraint loss function until the maximum loss satisfies a predetermined criterion.