Author/Authors :
nishanov, akhram khasanovich tashkent university of information technologies named after muhammad al-khwarizimi - department of software of information technologies, Tashkent, Uzbekistan , akbaraliev, bakhtiyorjon bakirovich tashkent university of information technologies named after muhammad al-khwarizimi - software engineering faculty, Tashkent, Uzbekistan , satimovich, samandarov batirbek karakalpak state university named after berdakh - department of applied mathematics, Nukus, Uzbekistan , kamarbekovich, akhmedov oybek tashkent university of information technology named after muhammad al-khwarizimi - department of systematic and practical programming, Tashkent, Uzbekistan , khudaybergenovich, tajibaev shukhrat karakalpak state university named after berdakh - department of applied mathematics, Nukus, Uzbekistan
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.
Keywords :
Classification and Localization of Objects , Qualitative and Quantitative Features , Evaluation of Objects and Features , Feature Selection
