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FSMEC: A Feature Selection Method based on the Minimum Spanning Tree and Evolutionary Computation
Zaher, A.A., Berretta, R., Arefin, A.S. and Moscato, P.
In feature selection we aim at reducing the dimensionality
of a dataset by excluding characteristics that do not
compromise, and potentially enhance, the classification of
a set of samples. We present a new type of supervised and
multivariate feature selection approach that works by
constructing proximity graphs in such a way that the
number of edges connecting samples from different
classes is minimised. We present this general idea using
the Minimum Spanning Tree as a proximity graph and an
Evolutionary Algorithm approach is used to search for a
feature subset. We compare the performance of our
algorithm against other feature selection methods,
(alpha,beta)-k-Feature Set, and a ranking-based feature
selection method, based on the use of CM1-scores. We
employ two publicly available real-world datasets (one
with training and test variants). The classification
accuracies have been evaluated using a total of 49
methods from an open source data mining and machine
learning package WEKA. |
Cite as: Zaher, A.A., Berretta, R., Arefin, A.S. and Moscato, P. (2015). FSMEC: A Feature Selection Method based on the Minimum Spanning Tree and Evolutionary Computation. In Proc. Thirteenth Australasian Data Mining Conference (AusDM 2015) Sydney, Australia. CRPIT, 168. Ong, K.L., Zhao, Y., Stone, M.G. and Islam, M.Z. Eds., ACS. 129-139 |
(from crpit.com)
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