Detecting Change via Competence Model

Publisher:
Springer
Publication Type:
Chapter
Citation:
Lecture Notes in Artificial Intelligence 6176 - Case-Based Reasoning, 2010, 1, pp. 201 - 212
Issue Date:
2010-01
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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 âwhenâ and âhowâ 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.
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