Abstract
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.
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Ślęzak, D., Widz, S. (2011). Rough-Set-Inspired Feature Subset Selection, Classifier Construction, and Rule Aggregation. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_13
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DOI: https://doi.org/10.1007/978-3-642-24425-4_13
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