Abstract
Selecting a set of representative prototypes in prediction systems enable us to generate prototype based rules (P-Rules), which constitute a very powerful means of providing domain experts with knowledge about the data and the process depicted by the data. P-rules has already proved very useful in classification tasks. This paper investigates application of P-rules to regression problems. The problem of our concern is prediction of oxygen activity in an electric arc furnace during steel scrap melting. For that purpose we use a new algorithm for determining prototype positions, which is based on conditional clustering. Also a comparison between the new algorithm and the classical clustering-based methods for prototype extraction is described.
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Blachnik, M., Kordos, M., Wieczorek, T., Golak, S. (2012). Selecting Representative Prototypes for Prediction the Oxygen Activity in Electric Arc Furnace. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_64
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DOI: https://doi.org/10.1007/978-3-642-29350-4_64
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