Improving Sequential Feature Selection Methods' Performance by Means of Hybridization

P. Somol, J. Novovičová, and P. Pudil (Czech Republic)


Feature selection, sequential search, hybrid methods, classification performance, subset search, statistical pattern recognition


In this paper we propose the general scheme of defining hy brid feature selection algorithms based on standard sequential search with the aim to improve feature selection performance, especially on high-dimensional or large-sample data. We show experimentally that “hybridization” has not only the potential to dramatically reduce FS search time, but in some cases also to actually improve classifier generalization, i.e., its classification performance on previously unknown data.

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