Year: 2020
Type: conference-paper
Abstract: In solving most of the practical problems associated with classification, a heuristic approach is widely used. Currently, many heuristic methods and algorithms based on various heuristic criteria have been developed. The most popular criteria are heuristic informational criteria. These criteria are related to the assessment of the separability of these classes and are based on the fundamental hypothesis of recognition compactness, i.e. with an increase in the relationship between classes, their separability improves. If traits maximize relationships, they are called "good." Such heuristic criteria are widely used and give good results in solving practical problems, but they are poorly studied in theoretical terms. At present, the method for choosing non-informative features, taking into account the relationship of features based on heuristic criteria, has not yet been developed. The article considers the problem of determining informative features by eliminating uninformative ones.
Authors: N Mamatov, A Samijonov, N Niyozmatova
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W3000448286