Booster of an FS Algorithm on High Dimensional Data

Authors

  • Sk. Shalini  M.Tech Scholar Department of CSE, NRI Institute of Technology Visadala(V&M),Guntur(Dt), Andhra Pradesh, India
  • K. Bhushanam  Assistant Professor Department of CSE, NRI Institute of Technology Visadala(V&M),Guntur(Dt), Andhra Pradesh, India

Keywords:

GPS tracking, Reliability, Road network, Visualized map, Map-matching, P-median Model, Network density.

Abstract

Classification issues in high dimensional knowledge with tiny variety of observations have become additional common particularly in microarray knowledge. The increasing quantity of text info on the net sites affects the agglomeration analysis[1]. The text agglomeration may be a favorable analysis technique used for partitioning a colossal quantity of knowledge into clusters. Hence, the most important downside that affects the text agglomeration technique is that the presence uninformative and distributed options in text documents .A broad class of boosting algorithms can be interpreted as performing coordinate-wise gradient descent to minimize some potential function of the margins of a data set[1]. This paper proposes a new evaluation measure Q-statistic that incorporates the stability of the selected feature subset in addition to the prediction accuracy. Then we propose the Booster of an FS algorithm that boosts the value of the Q statistic of the algorithm applied.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2018-08-30

Issue

Volume 4, Issue 9, July-August-2018

Section

Research Articles

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How to Cite

[1]
Sk. Shalini, K. Bhushanam "Booster of an FS Algorithm on High Dimensional Data" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 9, pp.496-500, July-August-2018.