Object
Title: Rough modeling - A bottom-up approach to model construction
Contributor:
Grzymala-Busse, Jerzy - ed. ; Świniarski, Roman W. - ed. ; Zhong, Ning - ed. ; Ziarko, Wojciech - ed.
Subtitle:
Rough Sets and Their Applications
Group publication title:
Abstract:
Traditional data mining methods based on rough set theory focus on extracting models which are good at classifying unseen objects. 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 and descriptive qualities, in addition to being computationally simple enough to handle large data sets. As rough models are exible 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.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Resource Identifier:
Pages:
Source:
AMCS, volume 11, number 3 (2001) ; click here to follow the link
Language:
Rights:
Biblioteka Uniwersytetu Zielonogórskiego
Object collections:
- Digital Library of Zielona Góra > Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Digital Library of Zielona Góra > Repository > Types of work > Articles
- Digital Library of Zielona Góra > Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2001)
Last modified:
Sep 3, 2021
In our library since:
Jul 21, 2021
Number of object content hits:
101
All available object's versions:
https://zbc.uz.zgora.pl/publication/65567
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International Journal of Applied Mathematics and Computer Science (AMCS)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 26 (2016)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 25 (2015)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 24 (2014)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 23 (2013)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 22 (2012)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 21 (2011)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 20 (2010)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 19 (2009)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 18 (2008)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 17 (2007)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 16 (2006)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 15 (2005)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 13 (2003)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 12 (2002)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 11 (2001)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 11, number 1 (2001)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 11, number 2 (2001)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 11, number 3 (2001)
- Rough sets methods in feature reduction and classification
- Rough set-based dimensionality reduction for supervised and unsupervised learning
- A rough set-based knowledge discovery process
- Rough relation properties
- Relative sets and rough sets
- Concept approximations based on rough sets and similarity measures
- Rough modeling - A bottom-up approach to model construction
- Minimal decision rules based on the Apriori algorithm
- Fuzzy versus probabilistic benefit/cost ratio analysis for public work projects
- Knowledge, vagueness and logic
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 11, number 4 (2001)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 11, number 6 (2001)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 11, number 1 (2001)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 7 (1997)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 6 (1996)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 5 (1995)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 4 (1994)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 3 (1993)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 2 (1992)
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International Journal of Applied Mathematics and Computer Science (AMCS),volume 1 (1991)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 26 (2016)
| Edition name | Date |
|---|---|
| Rough modeling - A bottom-up approach to model construction | Sep 3, 2021 |
