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SOME INVESTIGATIONS ON DIFFERENT CLASSIFICATION TECHNIQUES IN AGRICULTURE

KrishiKosh

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Title SOME INVESTIGATIONS ON DIFFERENT CLASSIFICATION TECHNIQUES IN AGRICULTURE
 
Creator SAMARENDRA DAS
 
Contributor Amrit Kumar Paul
 
Subject livestock, iron, manpower, area, biological phenomena, bears, rubber, body weight, cultivation, costs
 
Description RFT-3174
Classification is of broad interest in science because it leads to many scientific studies and
also arises in the contexts of many applications. For example in agriculture, crop varieties are
classified in to different groups which are suitable for different agroclimatic zones of a
region. Classification techniques are the multivariate techniques which are based on some
assumptions. But in crop data situation, there may be violation of assumptions of the
classification techniques. Hence, the purpose of the study to investigate how the classification
techniques perform when certain assumptions about the data characteristics are violated.
Further, occurrence of missing observations in multivariate crop data is a common feature in
agricultural experiments due to the non-response of genotypes, disease and pest attack. The
Classification of genotypes in presence of missing values is a challenging task for breeders.
So, the present investigation has been taken up to achieve the afore mentioned goal with the
following objectives: (i) To compare empirically the performance of Oblique Axes, k-th
nearest neighbor, Linear and Quadratic Discriminant procedures under multivariate skewnormal
situations. (ii) To assess the performance of different imputation techniques against
missing observations for different classification procedures. The performance of different
classification techniques viz. Oblique Axes Method (OAM), k-th nearest neighbor (KNN),
Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) are
compared based on Apparent classification Error Rate (APER). Further the result shows that,
KNN followed by OAM and LDA perform better in skew-normal situations than normal
condition and QDA performs better in normal condition. For maximum consistency and
accuracy of classification of skew-normal data, KNN is best among the above four
classification techniques. The performance of the above four classification techniques are
also studied under 1%, 5%, 10% and 20% missing observations created randomly in the
original data, which are imputed by different methods like zero, mean, regression and
multiple imputation methods based on the weighted average Hit Ratios. The present results
reveal that all the imputation methods are robust against 1% and 5% missing observations.
Further, it is found that mean, regression and multiple imputation techniques performs well
in case of 10% missing observations. In case of 20% or more missing observations the
regression and multiple imputation provide better results. Among four the classification
techniques, KNN technique performs best irrespective to the different levels of missing
observations.
 
Date 2016-12-15T14:23:50Z
2016-12-15T14:23:50Z
2011
 
Type Thesis
 
Identifier http://krishikosh.egranth.ac.in/handle/1/90328
 
Format application/pdf
 
Publisher iari, Indian Agricultural Statistics Research Institute