Large amount of data have been collected routinely in the course of
day-to-day work in different fields. Typically, the datasets
constantly grow accumulating a large number of features, which are
not equally important in decision-making. Rough set theory
(RST)recently becomes very popular in dimensionality reduction and
feature selection of large datasets. The RST approach to feature
selection is used to determine a subset of features (or attributes)
called reduct which can predict the decision concepts. In reality,
there are multiple reducts in a given information system used for
developing classifiers, amongst which the best performer is chosen
as the final solution to the problem. Selecting a reduct with good
performance is time expensive, as there might be many reducts of a
given dataset. Therefore, obtaining a best performer classifier is
not practical rather ensemble of different classifiers may lead to
better classification accuracy. However, combining large number of
classifiers increases complexity of the system. The work trades off
between these two approaches and creates an efficient ensemble
classifier.
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