# A Reinforcement Learning Approach to Online Learning of Decision Trees

@article{Garlapati2015ARL, title={A Reinforcement Learning Approach to Online Learning of Decision Trees}, author={Abhinav Garlapati and Aditi Raghunathan and Vaishnavh Nagarajan and Balaraman Ravindran}, journal={ArXiv}, year={2015}, volume={abs/1507.06923} }

Online decision tree learning algorithms typically examine all features of a new data point to update model parameters. We propose a novel alternative, Reinforcement Learning- based Decision Trees (RLDT), that uses Reinforcement Learning (RL) to actively examine a minimal number of features of a data point to classify it with high accuracy. Furthermore, RLDT optimizes a long term return, providing a better alternative to the traditional myopic greedy approach to growing decision trees. We… Expand

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