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Title: Locally Induced Predictive Models
Authors: Johansson, Ulf
Löfström, Tuve
Sönströd, Cecilia
Department: University of Borås. School of Business and IT
Issue Date: 2011
Citation: IEEE International Conference on Systems, Man, and Cybernetics
ISSN: 1062-922X
ISBN: 978-1-4577-0651-6
Pages: 1735-1740
Publisher: IEEE
Media type: text
Publication type: conference paper, peer reviewed
Keywords: local learning
predictive modeling
decision trees
rbf networks
Subject Category: Subject categories::Engineering and Technology::Computer and Information Science::Computer Science
Subject categories::Social Sciences::Computer and Information Science::Computer and Information Science
Research Group: CSL@BS
Area of Research: Machine Learning
Data Mining
Strategic Research Area: Business and IT
Abstract: Most predictive modeling techniques utilize all available data to build global models. This is despite the wellknown fact that for many problems, the targeted relationship varies greatly over the input space, thus suggesting that localized models may improve predictive performance. In this paper, we suggest and evaluate a technique inducing one predictive model for each test instance, using only neighboring instances. In the experimentation, several different variations of the suggested algorithm producing localized decision trees and neural network models are evaluated on 30 UCI data sets. The main result is that the suggested approach generally yields better predictive performance than global models built using all available training data. As a matter of fact, all techniques producing J48 trees obtained significantly higher accuracy and AUC, compared to the global J48 model. For RBF network models, with their inherent ability to use localized information, the suggested approach was only successful with regard to accuracy, while global RBF models had a better ranking ability, as seen by their generally higher AUCs.
DOI: 10.1109/ICSMC.2011.6083922
URI: http://hdl.handle.net/2320/10327
Appears in Collections:Konferensbidrag / Conference papers (Informatics)

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