Instance-Based Learning Algorithms
Machine Learning
A Nearest Hyperrectangle Learning Method
Machine Learning
Genetic programming: on the programming of computers by means of natural selection
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Machine Learning - Special issue on genetic algorithms
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A hybrid nearest-neighbor and nearest-hyperrectangle algorithm
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
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Evolutionary computation: toward a new philosophy of machine intelligence
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Data mining: practical machine learning tools and techniques with Java implementations
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
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Machine Learning
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Inducing Partially-Defined Instances with Evolutionary Algorithms
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
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GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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International Journal of Approximate Reasoning
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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. This paper presents an alternative way. The presented approach proposes to induce a reduced set of prototypes (partially-defined instances) with evolutionary algorithms. Experiments were performed with GALE, a fine-grained parallel evolutionary algorithm, and other well-known reduction techniques on several data sets. Results suggest that GALE is competitive and robust for inducing sets of partially-defined instances. Moreover, it achieves better reduction rates in storage requirements without losses in generalization accuracy. Simultaneously, if the partially-defined instances induced by GALE are post-processed, results can also be used for attribute selection.