Name
Title | ||
|---|---|---|
Is it important which rough-set-based classifier extraction and voting criteria are applied together? |
Abstract | ||
|---|---|---|
We propose a framework for experimental verification whether mechanisms of voting among rough-set-based classifiers and criteria for extracting those classifiers from data should follow analogous mathematical principles. Moreover, we show that some of types of criteria perform better for high-quality data while the others are useful rather for low-quality data. The framework is based on the principles of approximate attribute reduction and probabilistic extensions of rough-set-based approach to data analysis. The framework is not supposed to produce the best-ever classification results, unless it is extended by some additional parameters known from the literature. Instead, our major goal is to illustrate in a possibly simplistic way that it is worth unifying mathematical background for the stages of learning and applying rough-set-based classifiers. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13529-3_21 | RSCTC |
Keywords | Field | DocType |
data analysis,voting criterion,low-quality data,rough-set-based approach,approximate attribute reduction,additional parameter,analogous mathematical principle,rough-set-based classifier,high-quality data,best-ever classification result,classifier extraction,worth unifying mathematical background,rough set | Data mining,Voting,Computer science,Rough set,Artificial intelligence,Probabilistic logic,Classifier (linguistics),Machine learning | Conference |
Volume | ISSN | ISBN |
6086 | 0302-9743 | 3-642-13528-5 |
Citations | PageRank | References |
9 | 0.64 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dominik Ślęzak | 1 | 553 | 50.04 |
| Sebastian Widz | 2 | 67 | 6.50 |