Experiments with Two Approaches for Tracking Drifting Concepts

. This paper addresses the task of learning classifier from stream of labelled data. In this case we can face problem that the underling concepts can changes over time. The paper studies two mechanisms developed for dealing with changing concepts. Both are based on the time window idea. The first one forgets gradual, by assigning to the examples weight that gradually decreases over time. The second one uses a statistical test to detect changes in concept and then optimizes the size of time window, aiming to maximise the classification accuracy on the new examples. Both methods are general in nature and can be used with any learning algorithm. The objectives of the conducted experiments were to compare the mechanisms and explore whether they can combined to achieve a synergetic effect. Results from experiments with three basic learning algorithms (kNN, ID3 and NBC) using four datasets are reported and discussed

Discovering relationships among association rules over time

Master'sMASTER OF SCIENC

Temporal evolution and local patterns

Abstract We elaborate on the subject of pattern change as the result of population evolution. We give an overview of literature threads relevant to this subject, whereupon the major focus of related work is on pattern adaptation rather than on modeling and understanding change. We then describe our temporal model for patterns as evolving objects and propose criteria to capture the interestingness of pattern change and heuristics that trace interesting changes.