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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
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