Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching

Abstract

In their unmodified form, lazy-learning algorithms may have difficulty learning and tracking time-varying input/output function maps such as those that occur in concept shift. Extensions of these algorithms, such as Time-Windowed forgetting (TWF), can permit learning of time-varying mappings by deleting older exemplars, but have decreased classification accuracy when the input-space sampling distribution of the learning set is time-varying. Additionally, TWF suffers from lower asymptotic classification accuracy than equivalent non-forgetting algorithms when the input sampling distributions are stationary. Other shift-sensitive algorithms, such as Locally-Weighted forgetting (LWF) avoid the negative effects of time-varying sampling distributions, but still have lower asymptotic classification in non-varying cases. We introduce Prediction Error Context Switching (PECS) which allows lazy-learning algorithms to have good classification accuracy in conditions having a time-varying function mapping and input sampling distributions, while still maintaining their asymptotic classification accuracy in static tasks. PECS works by selecting and re-activating previously stored instances based on their most recent consistency record. The classification accuracy and active learning set sizes for the above algorithms are compared in a set of learning tasks that illustrate the differing time-varying conditions described above. The results show that the PECS algorithm has the best overall classification accuracy over these differing time-varying conditions, while still having asymptotic classification accuracy competitive with unmodified lazy-learners intended for static environments.