Computer Science and Information Systems 2014 Volume 11, Issue 2, Pages: 665-678
https://doi.org/10.2298/CSIS140212036O
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Efficient data abstraction using weighted IB2 prototypes
Ougiaroglou Stefanos (Department of Applied Informatics, School of Information Sciences, University of Macedonia, Thessaloniki, Greece)
Evangelidis Georgios (Department of Applied Informatics, School of Information Sciences, University of Macedonia, Thessaloniki, Greece)
Data reduction techniques improve the efficiency of k-Nearest Neighbour
classification on large datasets since they accelerate the classification
process and reduce storage requirements for the training data. IB2 is an
effective prototype selection data reduction technique. It selects some
items from the initial training dataset and uses them as representatives
(prototypes). Contrary to many other techniques, IB2 is a very fast,
one-pass method that builds its reduced (condensing) set in an incremental
manner. New training data can update the condensing set without the need of
the “old” removed items. This paper proposes a variation of IB2, that
generates new prototypes instead of selecting them. The variation is called
AIB2 and attempts to improve the efficiency of IB2 by positioning the
prototypes in the center of the data areas they represent. The empirical
experimental study conducted in the present work as well as the Wilcoxon
signed ranks test show that AIB2 performs better than IB2.
Keywords: k-NN classification, data reduction, abstraction, prototypes