A Reinforcement Learning Approach to Online Learning of Decision Trees
This work addresses the efficiency and adaptability of online decision tree learning for applications with streaming data, though it is incremental as it builds on existing reinforcement learning and decision tree methods.
The paper tackles the problem of online decision tree learning requiring examination of all features for each data point by proposing RLDT, which uses reinforcement learning to actively examine a minimal number of features, achieving comparable accuracy to batch and online algorithms while making significantly fewer feature queries and handling concept drift effectively.
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 demonstrate that this approach performs as well as batch learning algorithms and other online decision tree learning algorithms, while making significantly fewer queries about the features of the data points. We also show that RLDT can effectively handle concept drift.