Dynamic Learning Strategies for Data Mining

Provided by: Applications of Engineering Technology and Science (AETS)
Topic: Data Management
Format: PDF
A supervised learning algorithm aims to build a prediction model using training examples. This paradigm typically has the assumptions that the underlying distribution and the true input-output dependency does not change. However, these assumptions often fail to hold, especially in large datasets. This phenomenon is known as concept drift. The concept drift means that the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. In large data set, hidden patterns commonly evolve over time.

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