A Local Search with a Surrogate Assisted Option for Instance Reduction

In data mining, instance reduction is a key data pre-processing step that simplifies and cleans raw data, by either selecting or creating new samples, before applying a learning algorithm. This usually yields to a complex large scale and computationally expensive optimisation problem which has been typically tackled by sophisticated population-based metaheuristics. Unlike the recent literature, in order to accomplish this target, this article proposes the use of a simple local search algorithm and its integration with an optional surrogate assisted model. This local search, in accordance with variable decomposition techniques for large scale problems, perturbs an n-dimensional vector along the directions identified by its design variables one by one.

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