Open AccessReducing the dimensionality of feature space in pattern recognition tasks
Author(s) -
Sh.Kh. Fazilov,
N. Mamatov,
Samijonov Abdurashid,
Sh. Sh. Abdullaev
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1441/1/012139
Subject(s) - curse of dimensionality , pattern recognition (psychology) , computer science , artificial intelligence , set (abstract data type) , space (punctuation) , feature (linguistics) , feature vector , type (biology) , machine learning , philosophy , operating system , ecology , biology , programming language , linguistics
The definition of an informative set of features is one of the important tasks in pattern recognition. Typically, the determination of informative features is carried out using two types of methods. The first type of methods is “direct methods”, they are directly aimed at identifying informative sets of attributes. And the second type is called "inverse methods", these methods serve to build informative sets of signs by eliminating uninformative signs from the attribute space. This article is devoted specifically to the development of the second type of method; it proposes an accelerated method and an algorithm for determining non-informative features based on the selected non-informative criterion.