Abstract
Novelty detection, the ability to identify new or unknown situations that were never experienced before, is useful for intelligent systems aspiring to operate in environments where data are acquired incrementally. This characteristic is common to numerous problems in medical diagnosis and visual perception. We propose to see novelty detection as a case-based reasoning process. Our novelty-detection method is able to detect the novel situation, as well as to use the novel events for immediate reasoning. To ensure this capacity we combine statistical and similarity inference and learning. This view of CBR takes into account the properties of data, such as the uncertainty, and the underlying concepts, such as storage, learning, retrieval and indexing can be formalized and performed efficiently.
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Perner, P. (2007). Concepts for Novelty Detection and Handling Based on a Case-Based Reasoning Process Scheme. In: Perner, P. (eds) Advances in Data Mining. Theoretical Aspects and Applications. ICDM 2007. Lecture Notes in Computer Science(), vol 4597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73435-2_3
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DOI: https://doi.org/10.1007/978-3-540-73435-2_3
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