Drift mining in data: a framework for addressing drift in classification

DOI10.1016/j.csda.2012.07.007zbMath1365.62239OpenAlexW2038616484MaRDI QIDQ2359494

Vera Hofer, Georg Krempl

Publication date: 29 June 2017

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2012.07.007

zbMATH Keywords

concept driftverification latencydrift mining

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (4)

Adapting a classification rule to local and global shift when only unlabelled data are available ⋮ Stream-based active learning for sliding windows under the influence of verification latency ⋮ Unnamed Item ⋮ Unnamed Item

Cites Work

• Unnamed Item
• Unnamed Item
• Supervised and unsupervised ensemble methods and their applications
• Asymptotic results for goodness-of-fit statistics with unknown parameters
• On the application of ROC analysis to predict classification performance under varying class distributions
• A response to Webb and Ting's on the application of ROC analysis to predict classification performance under varying class distributions
• Transfer estimation of evolving class priors in data stream classification
• Measuring classifier performance: a coherent alternative to the area under the ROC curve
• Density estimation and comparison with a penalized mixture approach
• Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure
• Temporally-Adaptive Linear Classification for Handling Population Drift in Credit Scoring
• Generalized Linear Array Models with Applications to Multidimensional Smoothing
• Elements of Information Theory

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