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Feature vs. Classifier Fusion for Predictive Data - a Case Study in Pesticide Classification
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2007 (engelsk)Inngår i: Proceedings of the 10th International Conference on Information Fusion, IEEE , 2007, 1-7 s.Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Two strategies for fusing information from multiple sources when generating predictive models in the domain of pesticide classification are investigated: i) fusing different sets of features (molecular descriptors) before building a model and ii) fusing the classifiers built from the individual descriptor sets. An empirical investigation demonstrates that the choice of strategy can have a significant impact on the predictive performance. Furthermore, the experiment shows that the best strategy is dependent on the type of predictive model considered. When generating a decision tree for pesticide classification, a statistically significant difference in accuracy is observed in favor of combining predictions from the individual models compared to generating a single model from the fused set of molecular descriptors. On the other hand, when the model consists of an ensemble of decision trees, a statistically significant difference in accuracy is observed in favor of building the model from the fused set of descriptors compared to fusing ensemble models built from the individual sources.

sted, utgiver, år, opplag, sider
IEEE , 2007. 1-7 s.
HSV kategori
Identifikatorer
URN: urn:nbn:se:su:diva-37840DOI: 10.1109/ICIF.2007.4408024ISBN: 978-0-662-45804-3 (tryckt)OAI: oai:DiVA.org:su-37840DiVA: diva2:305368
Konferanse
Information Fusion, 2007 10th International Conference on 9-12 July 2007
Tilgjengelig fra: 2010-03-23 Laget: 2010-03-23 Sist oppdatert: 2011-06-28bibliografisk kontrollert

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