Enhancing Very Fast Decision Trees with Local Split-Time Predictions

An increasing number of industrial areas recognize the opportunities of Big Data, requiring highly efficient algorithms which enable real-time processing to reduce the burden of data storage and maintenance. Decision trees are extremely fast, highly accurate and easy to use in practice. Merging multiple decision trees to an ensemble leads to one of the most powerful machine learning methods. The Very Fast Decision Tree is the state-of-the-art incremental decision tree induction algorithm, capable of learning from massive data streams. It is successful due to its theoretical guarantees based on the Hoeffding bound as well as its competitive performance in terms of classification accuracy and time / space efficiency. In this paper, we increase the efficiency even further by replacing its global splitting scheme, which periodically tries to split every n_min examples. Instead, we utilize local statistics to predict the split-time, thus, avoiding unnecessary split-attempts, usually dominating the computational cost. Concretely, we use the class distributions of previous split-attempts to approximate the minimum number of examples until the Hoeffding bound is met. This cautious approach yields by design a low delay and reduces the number of split-attempts at the same time. We extensively evaluate our method using common stream-learning benchmarks also considering non-stationary environments. The experiments confirm a substantially reduced run-time without a loss in classification performance.