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- W1573201531 abstract "In real world applications, interested concepts are more likely to change rather than remain stable, which is known as concept drift. This situation causes problems on predictions for many learning algorithms including case-base reasoning (CBR). When learning under concept drift, a critical issue is to identify and determine “when” and “how” the concept changes. In this paper, we developed a competence-based empirical distance between case chunks and then proposed a change detection method based on it. As a main contribution of our work, the change detection method provides an approach to measure the distribution change of cases of an infinite domain through finite samples and requires no prior knowledge about the case distribution, which makes it more practical in real world applications. Also, different from many other change detection methods, we not only detect the change of concepts but also quantify and describe this change." @default.
- W1573201531 created "2016-06-24" @default.
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- W1573201531 date "2010-01-01" @default.
- W1573201531 modified "2023-09-25" @default.
- W1573201531 title "Detecting Change via Competence Model" @default.
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- W1573201531 doi "https://doi.org/10.1007/978-3-642-14274-1_16" @default.
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