Some Notes on Twenty One (21) Nearest Prototype Classifiers
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Condensed Nearest Neighbor Data Domain Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prototype selection based on sequential search
Intelligent Data Analysis
Using a genetic algorithm for editing k-nearest neighbor classifiers
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
A review of instance selection methods
Artificial Intelligence Review
Sequential reduction algorithm for nearest neighbor rule
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
Review: Supervised classification and mathematical optimization
Computers and Operations Research
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This paper presents a new approach to the selection of prototypes for the nearest neighbor rule which aims at obtaining an optimal or close-to-optimal solution. The problem is stated as a constrained optimization problem using the concept of consistency. In this context, the proposed method uses tabu search in the space of all possible subsets. Comparative experiments have been carried out using both synthetic and real data in which the algorithm has demonstrated its superiority over alternative approaches. The results obtained suggest that the tabu search condensing algorithm offers a very good tradeoff between computational burden and the optimality of the prototypes selected