Converting Feature Types in Analysis of Different Types of Data
D.Z. Narzullaev1, K.K. Shadmanov2, Sh.T. Ilyasov3
1Correspondence Author D.Z. Narzullaev*, Tashkent pharmaceutical Institute, Tashkent, Uzbekistan.
2K.K. Shadmanov, Tashkent pharmaceutical Institute, Tashkent, Uzbekistan.
3Sh.T. Ilyasov, Tashkent pharmaceutical Institute, Tashkent, Uzbekistan.
Manuscript received on January 17, 2020. | Revised Manuscript received on January 27, 2020. | Manuscript published on February 10, 2020. | PP: 421-426 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1441029420/2020©BEIESP| DOI: 10.35940/ijitee.D1441.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Today there are the large number of methods of data analysis for solving problems of pattern recognition of regression, correlation and factor analysis, which are not applicable in the case of different types of features in the source information. In this paper we propose an approach to solving this problem, named the conversion of feature types. The conversion of feature types is considered as an independent task that allows you to make the transition from non-quantitative features to quantitative ones and in further processing to apply the full range of classical methods of data analysis. The proposed algorithm is implemented in Delphi 10 Seattle the integrated software development sphere. The result of the study was tested when solving the task of recognition of several sets of known data.
Keywords: Data Analysis, Conversion of Feature Type, Pattern Recognition, Imperative Scale, Experimental Data Table.
Scope of the Article: Predictive Analysis