  We present a novel approach for the construction of ensemble classifiers
based on dimensionality reduction. Dimensionality reduction methods represent
datasets using a small number of attributes while preserving the information
conveyed by the original dataset. The ensemble members are trained based on
dimension-reduced versions of the training set. These versions are obtained by
applying dimensionality reduction to the original training set using different
values of the input parameters. This construction meets both the diversity and
accuracy criteria which are required to construct an ensemble classifier where
the former criterion is obtained by the various input parameter values and the
latter is achieved due to the decorrelation and noise reduction properties of
dimensionality reduction. In order to classify a test sample, it is first
embedded into the dimension reduced space of each individual classifier by
using an out-of-sample extension algorithm. Each classifier is then applied to
the embedded sample and the classification is obtained via a voting scheme. We
present three variations of the proposed approach based on the Random
Projections, the Diffusion Maps and the Random Subspaces dimensionality
reduction algorithms. We also present a multi-strategy ensemble which combines
AdaBoost and Diffusion Maps. A comparison is made with the Bagging, AdaBoost,
Rotation Forest ensemble classifiers and also with the base classifier which
does not incorporate dimensionality reduction. Our experiments used seventeen
benchmark datasets from the UCI repository. The results obtained by the
proposed algorithms were superior in many cases to other algorithms.
