Reinforced Decision Trees
This addresses the need for efficient multi-category classification, though it is incremental as it builds on existing tree-based and reinforcement learning techniques.
The paper tackles the problem of speeding up classification with many categories by proposing Reinforced Decision Trees, which simultaneously learn a tree structure for organizing categories and a classifier, achieving low inference complexity in a single learning step.
In order to speed-up classification models when facing a large number of categories, one usual approach consists in organizing the categories in a particular structure, this structure being then used as a way to speed-up the prediction computation. This is for example the case when using error-correcting codes or even hierarchies of categories. But in the majority of approaches, this structure is chosen \textit{by hand}, or during a preliminary step, and not integrated in the learning process. We propose a new model called Reinforced Decision Tree which simultaneously learns how to organize categories in a tree structure and how to classify any input based on this structure. This approach keeps the advantages of existing techniques (low inference complexity) but allows one to build efficient classifiers in one learning step. The learning algorithm is inspired by reinforcement learning and policy-gradient techniques which allows us to integrate the two steps (building the tree, and learning the classifier) in one single algorithm.