1 research outputs found
A Reinforcement Learning Approach to Online Learning of Decision Trees
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