Abstract
Concept drift has regained research interest during recent years as many applications use data sources that are changing over time. We study the classification task using logistic regression on a large news collection of 248K texts during a period of seven years. We present extrinsic methods of concept drift detection and quantification using training set formation with different windowing techniques. We characterize concept drift on a seven-year-long Le Monde news corpus and show the overestimation of classifier performance if it is neglected. We lay out paths for future work where we plan to refine extrinsic characterization methods and investigate the drifting of learning parameters when few examples are available.
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Šilić, A., Dalbelo Bašić, B. (2012). Exploring Classification Concept Drift on a Large News Text Corpus. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28604-9_35
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DOI: https://doi.org/10.1007/978-3-642-28604-9_35
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