Prototype induction and attribute selection via evolutionary algorithms
Intelligent Data AnalysisPublished 23 July 2003
Xavier Llorà, Josep M. Garrell
Citations16
SJR quartileQ3
SJR score0.29
SNIP0.45
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TL;DR
Results suggest that GALE is competitive and robust for inducing sets of partially-defined instances and achieves better reduction rates in storage requirements without losses in generalization accuracy.
Abstract
This paper addresses the issue of reducing the storage requirements on instance-based learning algorithms. Algorithms proposed by other researches use heuristics to prune instances of the training set or modify the instances themselves to achieve a r
Keywords
Computer Science
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