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Booster in High Dimensional Data Classification

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Booster in High Dimensional Data Classification


Paruchuri Geethika | Voleti Prasanthi

https://doi.org/10.31142/ijtsrd11368



Paruchuri Geethika | Voleti Prasanthi "Booster in High Dimensional Data Classification" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3, April 2018, pp.1186-1190, URL: https://www.ijtsrd.com/papers/ijtsrd11368.pdf

Classification issues in high dimensional information with modest number of perceptions are ending up additional normal particularly in microarray data. The increasing measure of content data on the Internet pages influences the grouping analysis[1]. The content grouping is a great examination method utilized for dividing an enormous measure of data into groups. Henceforth, the significant issue that influences the content grouping method is the nearness uninformative and inadequate highlights in content reports. A wide class of boosting calculations can be translated as performing coordinate-wise angle drop to limit some potential capacity of the edges of an information set[1]. This paper proposes another assessment measure Q-measurement that joins the solidness of the chose highlight subset in expansion to the expectation precision. At that point we propose the Booster of a FS calculation that lifts the estimation of the Qstatistic of the calculation connected.

high dimensional data classification; feature selection; stability; Q-statistic; Booster


IJTSRD11368
Volume-2 | Issue-3, April 2018
1186-1190
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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