Peter Stone's Selected Publications

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Automatic Feature Selection via Neuroevolution

Shimon Whiteson, Peter Stone, Kenneth O. Stanley, Risto Miikkulainen, and Nate Kohl. Automatic Feature Selection via Neuroevolution. In Proceedings of the Genetic and Evolutionary Computation Conference, June 2005.

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Abstract

Feature selection is the process of finding the set of inputs to a machine learning algorithm that will yield the best performance. Developing a way to solve this problem automatically would make current machine learning methods much more useful. Previous efforts to automate feature selection rely on expensive meta-learning or are applicable only when labeled training data is available. This paper presents a novel method called FS-NEAT which extends the NEAT neuroevolution method to automatically determine an appropriate set of inputs for the networks it evolves. By learning the network's inputs, topology, and weights simultaneously, FS-NEAT addresses the feature selection problem without relying on meta-learning or labeled data. Initial experiments in an autonomous car racing simulation demonstrate that FS-NEAT can learn better and faster than regular NEAT. In addition, the networks it evolves are smaller and require fewer inputs. Furthermore, FS-NEAT's performance remains robust even as the feature selection task it faces is made increasingly difficult.

BibTeX Entry

@InProceedings(GECCO05-fsneat,
        author="Shimon Whiteson and Peter Stone and Kenneth O.\ Stanley and Risto Miikkulainen and Nate Kohl",
        title="Automatic Feature Selection via Neuroevolution",
        booktitle="Proceedings of the Genetic and Evolutionary Computation Conference",
        month="June", year="2005",
        abstract={
   Feature selection is the process of finding the set of inputs to a
   machine learning algorithm that will yield the best performance.
   Developing a way to solve this problem automatically would make
   current machine learning methods much more useful.  Previous efforts
   to automate feature selection rely on expensive meta-learning or are
   applicable only when labeled training data is available.  This paper
   presents a novel method called FS-NEAT which extends the NEAT
   neuroevolution method to automatically determine an appropriate set
   of inputs for the networks it evolves.  By learning the network's
   inputs, topology, and weights simultaneously, FS-NEAT addresses the
   feature selection problem without relying on meta-learning or
   labeled data.  Initial experiments in an autonomous car racing
   simulation demonstrate that FS-NEAT can learn better and faster than
   regular NEAT.  In addition, the networks it evolves are smaller and
   require fewer inputs.  Furthermore, FS-NEAT's performance remains
   robust even as the feature selection task it faces is made
   increasingly difficult.
        },
)

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