Maximum Coverage Method for Feature Subset Selection for Neural Network Training
Keywords:Neural network, cluster, coverage, significant, shift, prediction, correctness, eliminating, separation
AbstractEvery 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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How to Cite
Boor, Štefan. (2012). Maximum Coverage Method for Feature Subset Selection for Neural Network Training. COMPUTING AND INFORMATICS, 30(5), 901–912. Retrieved from https://www.cai.sk/ojs/index.php/cai/article/view/202