The larger the size of the
data, structured or unstructured, the harder to understand and make use of it.
One of the fundamentals to machine learning is feature selection. Feature
selection, by reducing the number of irrelevant/redundant features,
dramatically reduces the run time of a learning algorithm and leads to a more
general concept. In this paper, realization of feature selection through a
neural network based algorithm, with the aid of a topology optimizer genetic
algorithm, is investigated. We have utilized NeuroEvolution of Augmenting
Topologies (NEAT) to select a subset of features with the most relevant
connection to the target concept. Discovery and improvement of solutions are
two main goals of machine learning, however, the accuracy of these varies
depends on dimensions of problem space. Although feature selection methods can
help to improve this accuracy, complexity of problem can also affect their
performance. Artificialneural networks are proven effective in feature
elimination, but as a consequence of fixed topology of most neural networks, it
loses accuracy when the number of local minimas is considerable in the problem.
To minimize this drawback, topology of neural network should be flexible and it
should be able to avoid local minimas especially when a feature is removed. In
this work, the power of feature selection through NEAT method is demonstrated.
When compared to the evolution of networks with fixed structure, NEAT discovers
significantly more sophisticated strategies. The results show NEAT can provide
better accuracy compared to conventional Multi-Layer Perceptron and leads to
improved feature selection.
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