Fast and Accurate Decision Trees for Natural Language Processing Tasks
Tiberiu Boroş, Ștefan Daniel Dumitrescu, Sonia Pipa · 2017
Decision trees have long been used in many machine-learning tasks; they have a clear structure that provides insight into the training data and are simple to conceptually understand and implement.We present an optimized tree-computation algorithm based on the original ID3 algorithm.We introduce a tree-pruning method that uses the development set to delete nodes from overfitted models, as well as a result-caching method for speedup.Our algorithm is 1 to 3 orders of magnitude faster than a naive implementation and yields accurate results on our test datasets.