Abstract
The paper presents a new condensation algorithm based on the idea of a sample representativeness. For each sample in a dataset a representative measure is counted. Starting with samples with the highest value of the measure, each sample and all its voters (which constitute single-class area) are condensed in one averaged prototype-sample. The algorithm is tested on nine well-known datasets and compared with Jozwik’s condensation methods.
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Raniszewski, M. (2011). Prototype Extraction of a Single-Class Area for the Condensed 1-NN Rule. In: Burduk, R., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Computer Recognition Systems 4. Advances in Intelligent and Soft Computing, vol 95. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20320-6_13
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DOI: https://doi.org/10.1007/978-3-642-20320-6_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20319-0
Online ISBN: 978-3-642-20320-6
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