Abstract

Every real object having certain properties can be described by a number of descriptors, visual or other, e.g. mechanical, chemical etc. A set of descriptors (features) characterizing a given object is described in the paper by a vector of descriptors, where each entry of the vector determines a value of some feature of the object. In general, it is important to describe the object as completely as possible, which means by a large number of descriptors. This paper deals with a problem of selection of a proper subset of descriptors, which have the most substantial influence on the properties of the object, so that irrelevant descriptors could be excluded. For this purpose, we introduce a new method, Maximum Coverage Method (MCM). This method has been combined with optimization by a classical genetic algorithm. The described method is used for a data pre-processing, with the resulting selected features serving as an input for a neural network.

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Cite this article

@article{cai-11035,
  author  = {Štefan Boor},
  title   = {Maximum Coverage Method for Feature Subset Selection for Neural Network Training},
  journal = {Computing and Informatics},
  volume  = {30},
  number  = {5},
  pages   = {901--912},
  year    = {2011},
  issn    = {1335-9150},
  url     = {https://cai.type.sk/content/2011/5/maximum-coverage-method-for-feature-subset-selection-for-neural-network-training/}
}
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