The problem of concept drift: definitions and related work

Alexey Tsymbal Department of Computer Science Trinity College Dublin, Ireland tsymbalo@tcd.ie April 29, 2004 Abstract In the real world concepts are often not stable but change with time. Typical examples of this are weather prediction rules and customers’ preferences. The underlying data distribution may change as well. Often these changes make the model built on old data inconsistent with the new data, and regular updating of the model is necessary. This problem, known as concept drift, complicates the task of learning a model from data and requires special approaches, different from commonly used techniques, which treat arriving instances as equally important contributors to the final concept. This paper considers different types of concept drift, peculiarities of the problem, and gives a critical review of existing approaches to the problem. 1. Definitions and peculiarities of the problem A difficult problem with learning in many real-world domains is that the concept of interest may depend on some hidden context, not given explicitly in the form of pre-dictive features. A typical example is weather prediction rules that may vary radically with the season. Another example is the patterns of customers’ buying preferences that may change with time, depending on the current day of the week, availability of alter-natives, inflation rate, etc. Often the cause of change is hidden, not known a priori, making the learning task more complicated. Changes in the hidden context can induce more or less radical changes in the target concept, which is generally known as con-cept drift (Widmer and Kubat, 1996). An effective learner should be able to track such changes and to quickly adapt to them. A difficult problem in handling concept drift is distinguishing between true concept drift and noise. Some algorithms may overreact to noise, erroneously interpreting it as concept drift, while others may be highly robust to noise, adjusting to the changes too slowly. An ideal learner should combine robustness to noise and sensitivity to concept drift (Widmer and Kubat, 1996). In many domains, hidden contexts may be expected to recur. Recurring contexts may be due to cyclic phenomena, such as seasons of the year or may be associated with irregular phenomena, such as inflation rates or market mood (Harries and Sam-mut, 1998). In such domains, in order to adapt more quickly to concept drift, concept