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Automatic Feature Selection via Neuroevolution.
Shimon
Whiteson, Peter Stone, Kenneth
O. Stanley, Risto Miikkulainen, and Nate
Kohl.
In Proceedings of the Genetic and Evolutionary Computation Conference, June 2005.
[PDF]181.4kB [postscript]1.6MB
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.
@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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