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our current approach with the single-label classifier ensemble ignores the taxonomy information. If instead we set up a tree of multilabel classifiers, we could enforce the taxonomy information. I talked this over with David, and here’s how it might go: a paper is passed to a root classifier, which predicts which Level 1 labels it would belong to (e.g., protect from harm, move). For each level 1 label the root classifier predicts, the paper will be routed to a specialized classifier – there would be a classifier which only takes “move” papers and predicts which Level 2 labels it would belong to (e.g., “move_through/on_solids”), and so on, recursively, until we end up with a set of Level 3 labels. You could think of the whole system as a hierarchy of gates, where a whole slew of papers comes to the main entrance (the root classifier) and gets filtered down into their respective destinations (labels) by increasingly specialized gates.
As currently described, the ensemble of single-label classifiers ignores relationships encoded in the hierarchy. In contrast, another innovation, a tree of multilabel classifiers, would encode the hierarchy explicitly into its own structure in order to enforce the taxonomy information.
In this conception, a biology paper is passed to a root classifier, which predicts which Level 1 labels the paper would apply to (e.g., “protect from harm”, “move”). For each Level 1 label that the root classifier predicts, the paper will be routed to a more specialized classifier. For example, there would be a classifier which only considers “move” papers and predicts which Level 2 labels it would belong to (e.g., “move through or on solids”), and so on, recursively, until the process ends with a set of Level 3 labels. Intuitively, the system is a hierarchy of gates, and a whole slew of papers arrives at the main entrance (the root classifier) and gets filtered down into their respective destinations (Level 3 labels) by increasingly specialized gates.
our current approach with the single-label classifier ensemble ignores the taxonomy information. If instead we set up a tree of multilabel classifiers, we could enforce the taxonomy information. I talked this over with David, and here’s how it might go: a paper is passed to a root classifier, which predicts which Level 1 labels it would belong to (e.g., protect from harm, move). For each level 1 label the root classifier predicts, the paper will be routed to a specialized classifier – there would be a classifier which only takes “move” papers and predicts which Level 2 labels it would belong to (e.g., “move_through/on_solids”), and so on, recursively, until we end up with a set of Level 3 labels. You could think of the whole system as a hierarchy of gates, where a whole slew of papers comes to the main entrance (the root classifier) and gets filtered down into their respective destinations (labels) by increasingly specialized gates.
related to/potentially supersedes #74
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