Dynamic Classifier Chain with Random Decision Trees

Classifiers chains (CC) is an effective approach in order to exploit label dependencies in multi-label data. However, it has the disadvantages that the chain is chosen at total random or relies on a pre-specified ordering of the labels which is expensive to compute. Moreover, the same ordering is used for every test instance, ignoring the fact that different orderings might be best suited for different test instances. We propose a new approach based on random decision trees (RDT) which can choose the label ordering for each prediction dynamically depending on the respective test instance. RDT are not adapted to a specific learning task, but in contrast allow to define a prediction objective on the fly during test time, thus offering a perfect test bed for directly comparing different prediction schemes. Indeed, we show that dynamically selecting the next label improves over using a static ordering of the labels under an otherwise unchanged RDT model and experimental environment.