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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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