Selection Of Informative Features using The Modified Version Of The Delta Method
Narzillo Mamatov1, Nilufar Niyozmatova2, Abdurashid Samijonov3, Zafar Yuldashev4, Musokhon Dadakhanov5
1Mamatov Narzillo*, Tashkent University Information Technologies named after Al-Kharezmi, Tashkent, Uzbekistan.
2Niyozmatova Nilufar, Tashkent University Information Technologies named after Al-Kharezmi, Tashkent,
3Samijonov Abdurashid, Bauman Moscow State Technical University, Moscow, Russia Federation.
4Yuldashev Zafar, Tashkent University Information Technologies named after Al-Kharezmi, Tashkent,
5Dadakhanov Musokhon, Namangan State University, Namangan,
Manuscript received on November 11, 2019. | Revised Manuscript received on 24 November, 2019. | Manuscript published on December 10, 2019. | PP: 3705-3708 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6590129219/2019©BEIESP | DOI: 10.35940/ijitee.B6590.129219
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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: Currently, the most common criteria for informative features are heuristic criteria related to assessing the separability of given classes and based on the compactness hypothesis fundamental in pattern recognition: with increasing distance between classes, their separability improves. “Good” are those signs that maximize this distance. Although heuristic criteria, although they are widely used in solving practical problems of classification, however, in theoretical terms they are little studied.
Keywords: Vector, Class, Criteria, Informative Features, Object, Function, Optimization Problem.
Scope of the Article: Design Optimization of Structures