Maximum Coverage Method for Feature Subset Selection for Neural Network Training

Year, pages: 2011, 901 - 912

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.

Boor, Š. 2011. Maximum Coverage Method for Feature Subset Selection for Neural Network Training. In Computing and Informatics, vol. 30, no.5, pp. 901-912. 1335-9150.

Boor, Š. (2011). Maximum Coverage Method for Feature Subset Selection for Neural Network Training. Computing and Informatics, 30(5), 901-912. 1335-9150.