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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2019)
[Effective from January 1, 2019]
For more details click here

ICV 2018: 95.39
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2018: 95.39
For more details click here

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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2020.9(2): 798-807
DOI: https://doi.org/10.20546/ijcmas.2020.902.097


Feature Selection for Discrimination between Low and High Oil Content Genotypes of Indian Mustard
Poonam Godara1*, B. K. Hooda1 and Ram Avtar2
1Department of Mathematics & Statistics, CCS, Haryana Agricultural University Hisar-125004 (Haryana), India
2Department of Genetics and Plant breeding, CCS, Haryana Agricultural University Hisar-125004 (Haryana), India
*Corresponding author
Abstract:

Variable selection in discriminant analysis may be used to identify those variables which are most relevant for use in allocating future observation. It is also expected to reduce the cost of experimentation and conditional error rate by increasing the ratio of the training sample size to the dimension. Thus, feature Selection has become important task in classification and discriminant analysis. Three variable selection methods (Univariate t-test, Wilk’s lambda Criterion and Random Forests Algorithm) were used and compared in the present study for classification and discrimination to find important characters of Indian mustard. Secondary data set on 310 genotypes of Indian mustard recorded for 12 characters was used for discrimination between populations of low and high oil content genotypes of Indian mustard. Performance of the methods was assessed in terms of leave one out cross-validation error and out of bag error rate for classification. The important variables for discrimination which significantly affected the oil content were siliqua length, Secondary branches, primary branches and days to maturity with least error rate of 33.90 per cent.


Keywords: Discriminant analysis, Error rates, Gini index, Random Forests, Wilk’s Lambda
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How to cite this article:

Poonam Godara, B. K. Hooda and Ram Avtar. 2020. Feature Selection for Discrimination between Low and High Oil Content Genotypes of Indian Mustard.Int.J.Curr.Microbiol.App.Sci. 9(2): 798-807. doi: https://doi.org/10.20546/ijcmas.2020.902.097