Instance-Based Learning Algorithms
Machine Learning
Selecting typical instances in instance-based learning
ML92 Proceedings of the ninth international workshop on Machine learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Pattern Recognition
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
Fast Nearest Neighbor Condensation for Large Data Sets Classification
IEEE Transactions on Knowledge and Data Engineering
Hit Miss Networks with Applications to Instance Selection
The Journal of Machine Learning Research
Graph-Based Discrete Differential Geometry for Critical Instance Filtering
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
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
A novel template reduction approach for the K-nearest neighbor method
IEEE Transactions on Neural Networks
Class Conditional Nearest Neighbor for Large Margin Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple-prototype classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
The reduced nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
An algorithm for a selective nearest neighbor decision rule (Corresp.)
IEEE Transactions on Information Theory
The condensed nearest neighbor rule using the concept of mutual nearest neighborhood (Corresp.)
IEEE Transactions on Information Theory
Adaptive nearest neighbor pattern classification
IEEE Transactions on Neural Networks
Density-Driven Generalized Regression Neural Networks (DD-GRNN) for Function Approximation
IEEE Transactions on Neural Networks
InstanceRank based on borders for instance selection
Pattern Recognition
A hybrid KNN-ant colony optimization algorithm for prototype selection
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
A novel prototype generation technique for handwriting digit recognition
Pattern Recognition
Prototype reduction based on Direct Weighted Pruning
Pattern Recognition Letters
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In instance-based machine learning, algorithms often suffer from storing large numbers of training instances. This results in large computer memory usage, long response time, and often oversensitivity to noise. In order to overcome such problems, various instance reduction algorithms have been developed to remove noisy and surplus instances. This paper discusses existing algorithms in the field of instance selection and abstraction, and introduces a new approach, the Class Boundary Preserving Algorithm (CBP), which is a multi-stage method for pruning the training set, based on a simple but very effective heuristic for instance removal. CBP is tested with a large number of datasets and comparatively evaluated against eight of the most successful instance-based condensation algorithms. Experiments showed that our algorithm achieved similar classification accuracies, with much improved storage reduction and competitive execution speeds.