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Bijanzadeh, E., Emam, Y., Ebrahimi, E., Ebrahimi, M.. (1391). Application of unsupervised weighting algorithms for identifying important attributes and factors contributing to grain and biological yields of wheat. سامانه مدیریت نشریات علمی, 2(2), 111-117. doi: 10.22092/cbj.2012.100427
E. Bijanzadeh; Y. Emam; E. Ebrahimi; M. Ebrahimi. "Application of unsupervised weighting algorithms for identifying important attributes and factors contributing to grain and biological yields of wheat". سامانه مدیریت نشریات علمی, 2, 2, 1391, 111-117. doi: 10.22092/cbj.2012.100427
Bijanzadeh, E., Emam, Y., Ebrahimi, E., Ebrahimi, M.. (1391). 'Application of unsupervised weighting algorithms for identifying important attributes and factors contributing to grain and biological yields of wheat', سامانه مدیریت نشریات علمی, 2(2), pp. 111-117. doi: 10.22092/cbj.2012.100427
Bijanzadeh, E., Emam, Y., Ebrahimi, E., Ebrahimi, M.. Application of unsupervised weighting algorithms for identifying important attributes and factors contributing to grain and biological yields of wheat. سامانه مدیریت نشریات علمی, 1391; 2(2): 111-117. doi: 10.22092/cbj.2012.100427

Application of unsupervised weighting algorithms for identifying important attributes and factors contributing to grain and biological yields of wheat

Crop Breeding Journal
مقاله 5، دوره 2، شماره 2، مهر 2012، صفحه 111-117    اصل مقاله (152.28 K)
شناسه دیجیتال (DOI): 10.22092/cbj.2012.100427
نویسندگان
E. Bijanzadeh* ؛ Y. Emam؛ E. Ebrahimi؛ M. Ebrahimi
Faculty of Agriculture, Shiraz University, Shiraz, Iran.
چکیده
To identify important attributes/factors that contribute to grain and biological yields of wheat, 9912 sets of diverse data from field studies were extracted, and supervised attribute-weighting models were employed. Results showed that when biological yield was the output, grain yield, nitrogen applied, rainfall, irrigation regime, and organic content were the most important factors/attributes, highlighted by 9, 7, 5, 3 and 3 weighting models, respectively. In contrast, when grain yield was the output, biological yield, location, and genotype were identified by 8, 6, and 5 weighting models, respectively. Also, five other features (cropping system, organic content, 1000-grain weight, spike number m-2 and soil texture) were selected by three models as the most important factors/attributes. Field water status, such as the irrigation regime or the amount of rainfall, was another important factor related to the biological or grain yield of wheat (weight ≥ 0.5). Our results showed that attribute/factor classification by unsupervised attribute-weighting models can provide a comprehensive view of the important distinguishing attributes/factors that contribute to wheat grain or biological yield. This is the first report on identifying the most important factors/attributes contributing to wheat grain and biological yields-using attribute-weighting algorithms. This study opened a new horizon in wheat production using data mining.
کلیدواژه‌ها
attribute weighting؛ Data Mining؛ unsupervised model؛ Wheat
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