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» Rough Set Approximation Based on Dynamic Granulation
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GECCO
2008
Springer
148views Optimization» more  GECCO 2008»
13 years 9 months ago
Accelerating convergence using rough sets theory for multi-objective optimization problems
We propose the use of rough sets theory to improve the first approximation provided by a multi-objective evolutionary algorithm and retain the nondominated solutions using a new ...
Luis V. Santana-Quintero, Carlos A. Coello Coello
RSKT
2010
Springer
13 years 7 months ago
Naive Bayesian Rough Sets
A naive Bayesian classifier is a probabilistic classifier based on Bayesian decision theory with naive independence assumptions, which is often used for ranking or constructing a...
Yiyu Yao, Bing Zhou
ISCI
2008
137views more  ISCI 2008»
13 years 8 months ago
Stochastic dominance-based rough set model for ordinal classification
In order to discover interesting patterns and dependencies in data, an approach based on rough set theory can be used. In particular, Dominance-based Rough Set Approach (DRSA) has...
Wojciech Kotlowski, Krzysztof Dembczynski, Salvato...
AI
1998
Springer
13 years 8 months ago
Uncertainty Measures of Rough Set Prediction
The main statistics used in rough set data analysis, the approximation quality, is of limited value when there is a choice of competing models for predicting a decision variable. ...
Ivo Düntsch, Günther Gediga
JACIII
2006
114views more  JACIII 2006»
13 years 8 months ago
A Theoretical Formulation of Object-Oriented Rough Set Models
ata forms, and abstract structural hierarchy based on is-a relationship and has-a relationship. Object structures illustrate many kinds of objects and actual dependence among objec...
Yasuo Kudo, Tetsuya Murai