PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Early Drift Detection Method
Manuel Baena-García, José del Campo-Ávila, Raul Fidalgo, Albert Bifet, Ricard Gavaldà and Rafael Morales-Bueno
In: ECML PKDD 2006 Workshop on Knowledge Discovery from Data Streams, 18 Set 2006, Berlin, Germany.

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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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:2509
Deposited By:Albert Bifet
Deposited On:23 November 2006