Advertisement
Published in:
2006 | OriginalPaper | Chapter
Modeling Uncertainty in Knowledge Discovery for Classifying Geographic Entities with Fuzzy Boundaries
Authors : Feng Qi, A-Xing Zhu
Published in: Progress in Spatial Data Handling
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.