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ICML
2001
IEEE

Inducing Partially-Defined Instances with Evolutionary Algorithms

10 years 4 months ago
Inducing Partially-Defined Instances with Evolutionary Algorithms
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 reduced set of instances. Our work presents an alternative way. We propose to induce a reduced set of partially-defined instances with Evolutionary Algorithms. Experiments were performed with GALE, our fine-grained parallel Evolutionary Algorithm, and other well-known reduction techniques on several datasets. Results suggest that Evolutionary Algorithms are competitive and robust for inducing sets of partially-defined instances, achieving better reduction rates in storage requirements without losses in generalization accuracy.
Josep Maria Garrell i Guiu, Xavier Llorà
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2001
Where ICML
Authors Josep Maria Garrell i Guiu, Xavier Llorà
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