Concept drift in machine learning and data mining refers to the change in the relationships between input and output data in the underlying problem over time.
In other domains, this change maybe called “ covariate shift ,” “ dataset shift ,” or “ nonstationarity .”
In most challenging data analysis applications, data evolve over time and must be analyzed in near real time. Patterns and relations in such data often evolve over time, thus, models built for analyzing such data quickly become obsolete over time. In machine learning and data mining this phenomenon is referred to as concept drift.
concept in “ concept drift ” refers to the unknown and hidden relationship between inputs and output variables.
For example, one concept in weather data may be the season that is not explicitly specified in temperature data, but may influence temperature data. Another example may be customer purchasing behavior over time that may be influenced by the strength of the economy, where the strength of the economy is not explicitly specified in the data. These elements are also called a “hidden context”.
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 predictive features. A typical example is weather prediction rules that may vary radically with the season. […] Often the cause of change is hidden, not known a priori, making the learning task more complicated.