عنوان مقاله [English]
The genus Rubus L. (Rosaceae, Rosoideae) includes 750 species. This genus is distributed from Low-TroPical to Sem-Polar region. Eight species and five hybridization varieties were reported in the flora of Iran. Rubus is one of the most challenging genera in flowering plants. Due to polyploids, apomixis and hybridization in the genus mentioned bring challenges in Rubus identification based on morphological characters. Collecting quantitative and qualitative data in plant studies is very time consuming and costly. Therefore, many kinds of research have been conducted on variable methods which are so reliable and economy vantage. Data mining has been applied for many purposes, e.g., bio-data analysis. In the current paper, a combination of different feature selection and classification algorithms was used to recognize the distinctive features of the genus Rubus L. Using the Random Forest classification method and the InfoGainAttributeEval feature selection model, we accurately classified it to 94.05 percent with 28 attributes which is the best algorithm in terms of accuracy and when we applied the MLP method and the SymetricalAttributeEval feature selection model, With only four attributes, the accuracy of the classification was obtained by 84.32 percent which is the algorithm with the least number of selected attributes. Four attributes mentioned were selected by most of the algorithms used in this paper. All of these attributes are qualitative and there is no need for laboratory measurement costs to obtain them. So there can be a suitable criterion for identifying key.
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