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title

CONTEMPLATE OF FEATURE REDUCTION USING NON PARAMETRIC STATISTICS USING DATA MINING ALGORITHMS

Author(s):

N.Himabindu

Keywords:

Classification, Dimensionality Reduction, k-Nearest Neighbor, Support Vector Machine, Random Forest, Principal Component Analysis

Abstract

Dimensionality Reduction is a procedure that undertakings to change over the information from high dimensional space to a less dimensional space while holding estimations among them and further advances the precision. In this survey paper diverse information mining arrangement procedures like k-Nearest Neighbor, Support Vector Machine, Random Forest, and Principal Component Analysis have been executed. This paper manages Attribute choice for Dimensionality diminishment in Machine learning. The test comes about are organized and charts show the execution of each of the method utilized. The Support Vector Machine furnishes better outcomes with most elevated exactness and minimum blunder rate, when contrasted and different classifiers.

Other Details

Paper ID: IJSARTV
Published in: Volume : 3, Issue : 11
Publication Date: 11/15/2017

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