Early Drift Detection Method

Code

• github.com/mitre/menelaus
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Abstract

An emerging problem in Data Streams is the detection of concept drift. This problem is aggravated when the drift is gradual over time. In this work we de¯ne a method for detecting concept drift, even in the case of slow gradual change. It is based on the estimated distribution of the distances between classi¯cation errors. The proposed method can be used with any learning algorithm in two ways: using it as a wrapper of a batch learning algorithm or implementing it inside an incremental and online algorithm. The experimentation results compare our method (EDDM) with a similar one (DDM). Latter uses the error-rate instead of distance-error-rate.

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