Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests
Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests
复制标题
利用模糊隶属函数改进玉米病虫害分类决策树模型的性能
DOI：
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发表时间：
2022
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通讯作者：
Des Alwine Zayantii
中科院分区：
文献类型：
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作者：
Y. Resti;C. Irsan;Muflika Amini;I. Yani;Rossi Passarella;Des Alwine Zayantii
玉米是一种重要的农业商品，因为它被用于动物饲料、生物燃料、工业加工以及淀粉、酸和酒精等非食品工业商品的制造。早期发现玉米病虫害的目的是减少作物歉收的可能性，保持作物产量的质量和数量。决策树是统计机器学习中的一种非参数分类模型，它使用树状结构决策来预测目标变量。如果将连续预测变量离散到有效的类别中，可以显著提高模型的性能。然而，在某些情况下，结果并不能提供令人满意的性能。可能的原因是离散预测变量的模糊性。在模型中引入模糊隶属函数，解决离散化模糊问题。本工作旨在利用决策树模型对玉米植物病虫害进行分类，并通过引入模糊隶属函数来提高模型的性能。这项工作的主要贡献是，我们通过实现模糊隶属函数，显着提高了决策树模型的性能；s型增长曲线、三角形曲线和s型收缩曲线。所提出的模糊模型优于决策树模型，其平均性能由大到小有所提高；kappa(12.16%)，召回率（11.8%），f评分（9.71%），精密度（5.08%），准确度（3.23%），特异性（1.94%），AUC（0.49%）。该模型产生的偏差和方差组合非常小，表明该模型能够很好地捕捉数据趋势。
Corn is an essential agricultural commodity since it is used in animal feed, biofuel, industrial processing, and the manufacture of non-food industrial commodities such as starch, acid, and alcohol. Early detection of diseases and pests of corn aims to reduce the possibility of crop failure and maintain the quality and quantity of crop yields. A decision tree is a nonparametric classification model in statistical machine learning that predicts target variables using tree-structured decisions. The performance of this model can increase significantly if the continuous predictor variables are discretized into valid categories. However, in some cases, the result does not provide satisfactory performance. The possible cause is the ambiguity in discretizing predictor variables. The incorporation of fuzzy membership functions into the model to resolve discretization ambiguity issues. This work aims to classify diseases and pests of corn plants using the decision tree model and improve the model’s performance by implementing fuzzy membership functions. The main contribution of this work is that we have shown a significant improvement in the decision tree model performance by implementing fuzzy membership functions; S-growth, triangle, and S-shrinkage curves. The proposed fuzzy model is better than the decision tree model, with an average performance increase from the largest to the smallest; kappa (12.16%), recall (11.8%), F-score (9.71%), precision (5.08%), accuracy (3.23%), specificity (1.94%), and AUC (0.49%). The combination of bias and variance generated by the proposed model is quite small, indicating that the model is able to capture data trends well.