Rough modeling - a bottom-up approach to model construction

• Autorzy: Terje Loken , Jan Komorowski
• Języki publikacji: EN

Abstrakty

EN

Traditional data mining methods based on rough set theory focus on extracting models which are good at classifying unseen obj-ects. If one wants to uncover new knowledge from the data, the model must have a high descriptive quality-it must describe the data set in a clear and concise manner, without sacrificing classification performance. Rough modeling, introduced by Kowalczyk (1998), is an approach which aims at providing models with good predictive emphand descriptive qualities, in addition to being computationally simple enough to handle large data sets. As rough models are flexible in nature and simple to generate, it is possible to generate a large number of models and search through them for the best model. Initial experiments confirm that the drop in performance of rough models compared to models induced using traditional rough set methods is slight at worst, and the gain in descriptive quality is very large.

Słowa kluczowe

EN

descriptive models; knowledge discovery; rough sets; rough modeling

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2001
• Tom: 11
• Numer: 3
• Strony: 675-690

Daty

wydano

2001

otrzymano

2001-03-01

poprawiono

2001-06-01

Twórcy

autor

Terje Loken

• Research and Development, FAST Search and Transfer ASA, Stoperigata 2, P.O. Box 1677 Vika, N-0120 Oslo, Norway

autor

Jan Komorowski

• Knowledge Systems Group, Department of Information and Computer Science, Norwegian University of Science and Technology, N-7491 Trondheim, Norway

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