Abstract
In many real-world situations there is no known method for computing the desired output from a set of inputs. A strategy for solving these type of problems is to learn the input-output functionality from examples. However, in such situations it is not known which information is relevant to the task at hand. In this paper we focus on selection of relevant examples. We propose a new noise elimination method which is based on the filtering of the so called pattern frequency domain and which resembles frequency domain filtering in signal and image processing. The proposed method is inspired by the bases selection algorithm. A basis is an irredundant set of relevant attributes. By identifying examples that are non-typical in bases determination, noise elimination is achieved. The empirical results show the effectiveness of the proposed example selection method on artificial and real databases.
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© 2002 Springer-Verlag Berlin Heidelberg
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Lashkia, G.V. (2002). A Noise Filtering Method for Inductive Concept Learning. In: Cohen, R., Spencer, B. (eds) Advances in Artificial Intelligence. Canadian AI 2002. Lecture Notes in Computer Science(), vol 2338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47922-8_7
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DOI: https://doi.org/10.1007/3-540-47922-8_7
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