Learning Decision Trees Adaptively from Data Streams with Time Drift
Albert Bifet and Ricard Gavaldà
We propose a new method for mining concept-drifting
data streams using decision trees and adaptive windowing.
We present a new algorithm based on Hulten-Spencer-
Domingos’s CVFDT that overcomes some of the shortcomings
of CVFDT, specifically, dependence on user-entered
parameters that determine the guessed speed of change.
Our algorithm detects when change occurs and provably
adapts to the speed of change without user intervention.
It is based on ADWIN, an adaptive algorithm for detecting
change and maintaining an updated sample from the input
sequence automatically. Our experiments show that the new
algorithm does never worse, and in some cases much better,