Case-based reasoning
Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Reasoning about Gradual Changes of Topological Relationships
Proceedings of the International Conference GIS - From Space to Territory: Theories and Methods of Spatio-Temporal Reasoning on Theories and Methods of Spatio-Temporal Reasoning in Geographic Space
Similarity assessment for cardinal directions between extended spatial objects
Similarity assessment for cardinal directions between extended spatial objects
Image processing in case-based reasoning
The Knowledge Engineering Review
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Predicting business failure using multiple case-based reasoning combined with support vector machine
Expert Systems with Applications: An International Journal
Remote sensing data change detection based on the CI test of Bayesian networks
Computers & Geosciences
A case-based reasoning approach for land use change prediction
Expert Systems with Applications: An International Journal
A case-based classifier for hypertension detection
Knowledge-Based Systems
A knowledge-based problem solving method in GIS application
Knowledge-Based Systems
Coupling bayesian networks with GIS-Based cellular automata for modeling land use change
GIScience'06 Proceedings of the 4th international conference on Geographic Information Science
A method for extracting rules from spatial data based on rough fuzzy sets
Knowledge-Based Systems
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Case-based reasoning (CBR) method has been widely used to study environmental and spatial problems since the 1990s. Spatial relations among geographic cases and between case and environment (hereinafter to be referred as spatial relations) were not well considered in most of the previous studies. However, these relations are extremely important in geographic problems solving as spatially closer geographic phenomena are more likely to be similar than those are disperse in space. This paper presents a generic application paradigm based on CBR to solve geographic problems. To better consider the spatial relations, a new component of ''Geographic Environment'' was added into the standard CBR case representation model. A rough set-based algorithm was used to prune essential spatial relations, which were then used to extract key decision rules and retrieve similar past cases for the new problem. Standard CBR directly accepts the solution that was derived from the past similar cases to solve the new problem. In this study, however, solution was not accepted unless it also satisfied the key decision rules. An illustrating example was used to demonstrate how the general framework and algorithm could be applied to solve geographic problems. The algorithm was then evaluated by examining two datasets, the 2003 land use in the Pearl River Estuary and land use change from 1995 to 2000 in Zhuhai city of China. These two datasets were also examined by the standard CBR and Bayesian Networks (BNs) methods. Comparison of the stratified 10-fold cross-validation results indicated that the algorithm proposed in this study yielded statistically significant higher overall validation accuracy than the standard CBR and BNs methods.