Drift mining in data: A framework for addressing drift in classification

A novel statistical methodology for analysing population drift in classification is introduced. Drift denotes changes in the joint distribution of explanatory variables and class labels over time. It entails the deterioration of a classifier’s performance and requires the optimal decision boundary to be adapted after some time. However, in the presence of verification latency a re-estimation of the classification model is impossible, since in such a situation only recent unlabelled data are available, and the true corresponding labels only become known after some lapse in time. For this reason a novel drift mining methodology is presented which aims at detecting changes over time. It allows us either to understand evolution in the data from an ex-post perspective or, ex-ante, to anticipate changes in the joint distribution. The proposed drift mining technique assumes that the class priors change by a certain factor from one time point to the next, and that the conditional distributions do not change within this time period. Thus, the conditional distributions can be estimated at a time where recent labelled data are available. In subsequent periods the unconditional distribution can be expressed as a mixture of the conditional distributions, where the mixing proportions are equal to the class priors. However, as the unconditional distributions can also be estimated from new unlabelled data, they can then be compared to the mixture representation by means of least-squares criteria. This allows for easy and fast estimation of the changes in class prior values in the presence of verification latency. The usefulness of this drift mining approach is demonstrated using a real-world dataset from the area of credit scoring.