A COMPARISON OF MISSING DATA HANDLING TECHNIQUES
Abstract
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Missing data is a regular concern on data that professionals have to deal with. Efficient analysis techniques have to be followed to find interesting patterns. In this study, we are comparing 16 different imputation methods namely Linear, Index, Values, Nearest, Zero, slinear, Quadratic, Cubic, Barycentric, Krogh, Polynomial, Spline, Piecewise Polynomial, From derivatives, Pchip and Akima. These techniques are performed on real time UCI dataset and are under Missing Completely at a Random (MCAR) assumption, our result suggests the nearest, zero, quadratic and polynomial imputation methods which provides above 96% of accuracy when compared to the other techniques.

Authors
S David Samuel Azariya1,V Mohanraj2, J Jeba Emilyn3, G Jothi 4
Sona College of Technology, India 1, Sona College of Technology, India 2, Sona College of Technology, India 3, Sona College of Arts and Science, India /4

Keywords
Plant Disease, Plant Leaf, Recognition, Clustering
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Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 11 , Issue: 4 , Pages: 2429-2432 )
Date of Publication :
July 2021
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110
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1

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