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Feature vs. Classifier Fusion for Predictive Data - a Case Study in Pesticide Classification
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2007 (English)In: Proceedings of the 10th International Conference on Information Fusion, IEEE , 2007, 1-7 p.Conference paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE , 2007. 1-7 p.
National Category
Information Science
URN: urn:nbn:se:su:diva-37840DOI: 10.1109/ICIF.2007.4408024ISBN: 978-0-662-45804-3OAI: diva2:305368
Information Fusion, 2007 10th International Conference on 9-12 July 2007
Available from: 2010-03-23 Created: 2010-03-23 Last updated: 2011-06-28Bibliographically approved

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Boström, Henrik
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Department of Computer and Systems Sciences
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