A Parallel Feature Selection Algorithm from Random Subsets
نویسندگان
چکیده
Feature selection methods are used to find the set of features that yield the best classification accuracy for a given data set. This results in lower training and classification time for a classifier, a support vector machine here, and better classification accuracy. Feature selection, however, may be a time consuming process unfit for real time application. In this paper, we explore a feature selection algorithm based on support vector machine training time. It is compared with the Wrapper algorithm. Our approach can be run on all available processors in parallel. Our feature selection approach is ideal if new features need to be selected during data acquisition, where a fast, approximate approach may be advantageous. Experimental results indicate that the training time based method yields feature sets almost as good as the Wrapper method, while requiring considerably less computation time.
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