A Lazy Ensemble Learning Method to Classification
Depending on how a learner reacts to the test instances, supervised
learning divided into eager learning and lazy learning. Lazy
learners endeavor to find local optimal solutions for each particular
test instance. Many approaches for constructing lazy learning have
been developed, one of the successful one is to incorporate lazy
learning with ensemble classification. Almost all lazy learning
schemes are suffering from reduction in classifier diversity.
Diversity among the members of a team of classifiers is deemed to
be a key issue in classifier combination. In this paper we proposed
a Lazy Stacking approach to classification, named LS. To keep the
diversity of classifiers at a desire level, LS utilizes different
learning schemes to build the base classifiers of ensemble. To
investigate LS's performance, we compare LS against four rival
algorithms on a large suite of 12 real-world benchmark datasets.
Empirical results confirm that LS can statistically significantly
outperform alternative methods in terms of classification accuracy.
Keywords: Classification, diversity, classifier ensemble, stacking,
lazy learning
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