Abstract
Data preprocessing is an essential step of the KDD process. It makes it possible to extract useful information from data. We propose two coefficients which respectively study the informational contribution of initial data in supervised learning and the intrinsic structure of initial data in not supervised one. These coefficients are based on Kappa coefficient. The confrontation of these two coefficients enables us to determine if feature construction is useful. We can present a system allowing the optimization of preprocessing step : feature selection is applied in all cases; then the two coefficients are calculated for the selected features. With the comparison of the two coefficients, we can decide the importance of feature construction.
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© 2005 Springer-Verlag Berlin Heidelberg
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Legrand, G., Nicoloyannis, N. (2005). Data Preprocessing and Kappa Coefficient. In: Ślęzak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548669_19
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DOI: https://doi.org/10.1007/11548669_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28653-0
Online ISBN: 978-3-540-31825-5
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