An Algorithm for Classification, Localization and Selection of Informative Features in the Space of Politypic Data
Journal Title: Webology - Year 2020, Vol 17, Issue 1
Abstract
Dimensionality reduction and feature subset selection are very important and challenging issues in preliminary processing of the large amount of data for its intellectual analysis, pattern recognition and clustering. In particular, the relevance of these issues will only grow if the preliminary data is derived from real-life and defined by qualitative indictors. Similarly, when problem is related to the selection of complex of important features for classification and localization of agricultural crops, the results are immediately reflected in practice. Thus, the problem would appear unclear should the initial data be polytypic i.e. defined by quality indicators and quantitative features. To address the problem, algorithms and programs have been developed for determining the degree of similarity of the objects based on analysis of the existing literature, and then textual, nominal and quantitative definition of the features in the form of qualitative indicators, and mathematical interpretation of the problem.
Authors and Affiliations
Akhram Khasanovich Nishanov, Bakhtiyorjon Bakirovich Akbaraliev, Samandarov Batirbek Satimovich, Akhmedov Oybek Kamarbekovich and Tajibaev Shukhrat Khudaybergenovich
Keywords
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- EP ID EP687857
- DOI 10.14704/WEB/V17I1/WEB17009
- Views 229
- Downloads 0
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