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Dynamic Class Prediction with Classifier Based Distance Measure
Saglam, S.Y. and Street, W.N.
Combining multiple classifiers (ensemble of classifiers)
to make predictions for new instances has shown
to outperform a single classifier. As opposed to using
the same ensemble for all data instances, recent
studies have focused on dynamic ensembles in which
a new ensemble is chosen from a pool of classifiers
specifically for every new data instance. We propose
a system for dynamic class prediction based on a new
distance measure to evaluate the distance among data
instances. We first map data instances into a space defined
by the class probability estimates from a pool of
two-class classifiers. We dynamically pick classifiers
(features) to be used and the k-nearest neighbors of a
new instance by minimizing the distance between the
neighbors and that instance in a two-step framework.
Results of our experiments show that our measure is
effective for finding similar instances and our framework
helps making more accurate predictions. |
Cite as: Saglam, S.Y. and Street, W.N. (2014). Dynamic Class Prediction with Classifier Based Distance Measure. In Proc. Twelfth Australasian Data Mining Conference (AusDM14) Brisbane, Australia. CRPIT, 158. Li, X., Liu, L., Ong, K.L. and Zhao, Y. Eds., ACS. 79-90 |
(from crpit.com)
(local if available)
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