Abstract
Boosting is a machine learning strategy originally designed to increase classification accuracies of classifiers through inductive learning. This paper argues that this strategy of learning and inference actually corresponds to a cognitive model that explains the uncertainty associated with class assignments for classifying geographic entities with fuzzy boundaries. This paper presents a study that adopts the boosting strategy in knowledge discovery, which allows for the modeling and mapping of such uncertainty when the discovered knowledge is used for classification. A case study of knowledge discovery for soil classification proves the effectiveness of this approach.
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Qi, F., Zhu, AX. (2006). Modeling Uncertainty in Knowledge Discovery for Classifying Geographic Entities with Fuzzy Boundaries. In: Riedl, A., Kainz, W., Elmes, G.A. (eds) Progress in Spatial Data Handling. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-35589-8_46
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DOI: https://doi.org/10.1007/3-540-35589-8_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35588-5
Online ISBN: 978-3-540-35589-2
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