Instance-Based Learning Algorithms
Machine Learning
An Efficient and Effective Procedure for Updating a Competence Model for Case-Based Reasoners
ECML '00 Proceedings of the 11th European Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
On the Consistency of Information Filters for Lazy Learning Algorithms
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Modelling the Competence of Case-Bases
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Competence-Guided Case-Base Editing Techniques
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Building Compact Competent Case-Bases
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Competence Model and Their Applications
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Competence-Guided Case-Base Editing Techniques
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Releasing Memory Space through a Case-Deletion Policy with a Lower Bound for Residual Competence
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Catching the Drift: Using Feature-Free Case-Based Reasoning for Spam Filtering
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
An Approach to Software Design Reuse Using Case-Based Reasoning and WordNet
Proceedings of the 2006 conference on Integrated Intelligent Systems for Engineering Design
Maintenance by a Committee of Experts: The MACE Approach to Case-Base Maintenance
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
An Integrated Knowledge Adaption Framework for Case-Based Reasoning Systems
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Noise reduction for instance-based learning with a local maximal margin approach
Journal of Intelligent Information Systems
Evaluating case selection algorithms for analogical reasoning systems
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Complexity profiling for informed case-base editing
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Modified blame-based noise reduction for concept drift
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
On dataset complexity for case base maintenance
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
jcolibri2: A framework for building Case-based reasoning systems
Science of Computer Programming
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Case-based classification is a powerful classification method, which (in its simplest form) assigns a target case to the same class as the nearest of n previously classified cases. Many case-based classifiers use the simple nearest-neighbour technique to identify the nearest case, but this means comparing the target case to all of the stored cases at classification time, resulting in high classification costs. For this reason many techniques have been proposed to improve the performance of case-based classifiers by reducing the search they must perform. In this paper we will look at editing techniques that preserve the lazy-learning quality of case-based classification, but improve classification performance.