การเปรียบเทียบประสิทธิภาพในการทำนายผลการจำแนกเมล็ดพันธุ์ข้าวเปลือกขาวดอกมะลิ 105 ด้วยเทคนิคการทำเหมืองข้อมูล
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Abstract
In this study, an efficiency comparison in prediction of Khao Dok Mali 105 paddy rice classification with data mining techniques was compared. The seven classification methods were the followings: (1) k-nearest neighbor method using IBk algorithm; (2) decision tree method using J48 algorithm; (3) artificial neural network method using multilayer perceptron algorithm; (4) support vector machine method using polynomial kernel; (5) rule-based method using decision table algorithm; (6) binary logistic regression method; and (7) naïve Bayes method. The following efficiency comparisons of classification were employed: accuracy, recall, F-measure, and mean square error (MSE). The important results are as follows. The k-nearest neighbor method using random seed = 10, 20 and 30 showed the best accuracy, recall, F-measure, and MSE at 100 %, 1.000, 1.000 and 0.00002 respectively. The support vector machine method and rule-based using random seed = 10, and 20 exhibited the best recall at 1.000. Since the k-nearest neighbor method offered the best efficiencies for all the 4 values, it was considered the best prediction method.
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References
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