# Rough modeling - a bottom-up approach to model construction

International Journal of Applied Mathematics and Computer Science (2001)

- Volume: 11, Issue: 3, page 675-690
- ISSN: 1641-876X

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topLoken, Terje, and Komorowski, Jan. "Rough modeling - a bottom-up approach to model construction." International Journal of Applied Mathematics and Computer Science 11.3 (2001): 675-690. <http://eudml.org/doc/207526>.

@article{Loken2001,

abstract = {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.},

author = {Loken, Terje, Komorowski, Jan},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {descriptive models; knowledge discovery; rough sets; rough modeling; rough set theory; rough data models},

language = {eng},

number = {3},

pages = {675-690},

title = {Rough modeling - a bottom-up approach to model construction},

url = {http://eudml.org/doc/207526},

volume = {11},

year = {2001},

}

TY - JOUR

AU - Loken, Terje

AU - Komorowski, Jan

TI - Rough modeling - a bottom-up approach to model construction

JO - International Journal of Applied Mathematics and Computer Science

PY - 2001

VL - 11

IS - 3

SP - 675

EP - 690

AB - 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.

LA - eng

KW - descriptive models; knowledge discovery; rough sets; rough modeling; rough set theory; rough data models

UR - http://eudml.org/doc/207526

ER -

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