Application of unsupervised weighting algorithms for identifying important attributes and factors contributing to grain and biological yields of wheat
- سال انتشار: 1391
- محل انتشار: مجله به نژادی محصولات، دوره: 2، شماره: 2
- کد COI اختصاصی: JR_CBJOU-2-2_005
- زبان مقاله: انگلیسی
- تعداد مشاهده: 73
نویسندگان
Faculty of Agriculture, Shiraz University, Shiraz, Iran.
چکیده
To identify important attributes/factors that contribute to grain and biological yields of wheat, ۹۹۱۲ 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 ۹, ۷, ۵, ۳ and ۳ weighting models, respectively. In contrast, when grain yield was the output, biological yield, location, and genotype were identified by ۸, ۶, and ۵ weighting models, respectively. Also, five other features (cropping system, organic content, ۱۰۰۰-grain weight, spike number m-۲ 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 ≥ ۰.۵). 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.کلیدواژه ها
attribute weighting, Data Mining, unsupervised model, Wheatاطلاعات بیشتر در مورد COI
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