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Estimating the Expected Effectiveness of Text Classification Solutions under Subclass Distribution Shifts
2012
2012 IEEE 12th International Conference on Data Mining
Automated text classification is one of the most important learning technologies to fight information overload. However, the information society is not only confronted with an information flood but also with an increase in "information volatility", by which we understand the fact that kind and distribution of a data source's emissions can significantly vary. In this paper we show how to estimate the expected effectiveness of a classification solution when the underlying data source undergoes a
doi:10.1109/icdm.2012.89
dblp:conf/icdm/LipkaSS12
fatcat:uodau3fiqnhzbgxu4w6ipmj7py