Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains
This work addresses multi-label classification by improving label dependency handling with dynamic ordering, offering incremental enhancements for practitioners needing efficient and customizable models.
The paper tackles the problem of static label ordering in classifier chains for multi-label classification by proposing dynamic classifier chains (DCC) integrated with XGBoost, resulting in a fast and scalable method that reduces training costs and allows control over optimization measures, as shown in experiments on eleven datasets.
Classifier chains is a key technique in multi-label classification, since it allows to consider label dependencies effectively. However, the classifiers are aligned according to a static order of the labels. In the concept of dynamic classifier chains (DCC) the label ordering is chosen for each prediction dynamically depending on the respective instance at hand. We combine this concept with the boosting of extreme gradient boosted trees (XGBoost), an effective and scalable state-of-the-art technique, and incorporate DCC in a fast multi-label extension of XGBoost which we make publicly available. As only positive labels have to be predicted and these are usually only few, the training costs can be further substantially reduced. Moreover, as experiments on eleven datasets show, the length of the chain allows for a more control over the usage of previous predictions and hence over the measure one want to optimize.