Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Rough set algorithms in classification problem
Rough set methods and applications
Data mining based on rough sets
Data mining
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
A Rough Set Approach to Multiple Classifier Systems
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
Rough Sets and Functional Dependencies in Data: Foundations of Association Reducts
Transactions on Computational Science V
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
| Hi-index | 0.00 |
We consider a rough-set-inspired framework for deriving feature subset ensembles from data. Each of feature subsets yields a single classifier, basically by generating its corresponding if-then decision rules from the training data. Feature subsets are extracted according to a simple randomized algorithm, following the filter (rather than wrapper or embedded) methodology. Classifier ensemble is built from single classifiers by defining aggregation laws on top of decision rules. We investigate whether rough-set-inspired methods can help in the steps of formulating feature subset optimization criteria, feature subset search heuristics, and the strategies of voting among classifiers. Comparing to our previous research, we pay a special attention to synchronization of the filter-based criteria for feature subset selection and extraction of rules basing on the obtained feature subsets. The overall framework is not supposed to produce the best-ever classification results, unless it is extended by some additional techniques known from the literature. Our major goal is to illustrate in a possibly simplistic way some general interactions between the above-mentioned criteria.