Local Space-Time Smoothing for Version Controlled Documents
Figure 6: Error rate and F1 measure over held out test set of predicting future UNDO operation in Wikipedia articles. Method a corresponds to a predictor that always selects the majority class. Method b corresponds to a logistic regression based on the term frequency vector of the current version. Method c corresponds a logistic regression that uses summaries (mean, median, max, min) of ∥˙γs(s,t)∥, ∥˙γs(s,t)∥, g(t), and h(s).
['[BOLD] Article', '[BOLD] Rev.', '[BOLD] Voc.  [BOLD] Size', '[ITALIC]  [BOLD] p [BOLD] ( [ITALIC]  [BOLD] y [BOLD] )', '[BOLD] Error Rate a', '[BOLD] Error Rate b', '[BOLD] Error Rate c', '[BOLD] F1 Measure a', '[BOLD] F1 Measure b', '[BOLD] F1 Measure c']
We predict whether a version will undergo UNDO in the next version using a support vector machine based on statistical summaries (mean, median, min, max) of the following feature set ∥˙γs(s,t)∥, ∥¨γs(s,t)∥, ∥˙γt(s,t)∥), ∥˙γt(s,t)∥, g(h), and h(s). The first baseline (method a) predicts the majority class and the second baseline (method b) is a logistic regression based on the term frequency content of the current test version. Using the derivatives of γ, we obtain a prediction that is better than choosing majority class or logistic regression based on word content. We thus conclude that the derivatives above provide more useful information (resulting in lower error and higher F1) for predicting future operations than word content features.