การเปรียบเทียบประสิทธิภาพในการทำนายผลการปรับความไม่สมดุลของข้อมูลในการจำแนกด้วยเทคนิคการทำเหมืองข้อมูล - ทองพูล พัชรียา; จำเรือง พิมพ์ชนก; บุญฤทธิ์ รมย์นลิน; สินสมบูรณ์ทอง สายชล (Thai Journal of Science and Technology, 2019)
Abstract We compared the imbalanced data of four methods: over sampling, synthetic minority over sampling technique, under sampling and hybrid method using four classification methods: k-nearest neighbor, decision tree, artificial neural network and support vector machine. Metrics were accuracy, sensitivity, specificity and mean squared error. The data sets were auditory perception in children with otitis media with effusion (OME), credit card balance and red wine quality. Each of these data sets was divided into three proportions in the ratio of 70:20:10 using the data part 1, training data are used to create a model 70 percentages; the data part 2, validaion data are used to evaluate an error a model 20 percentages and the data part 3, testing data are used to testing a model 10 percentages using the random seed 10, 20, 30, 40 and 50 by WEKA program. When we compared the OME data set, the best classification method was the support vector machine in imbalanced data, adapting the synthetic minority over sampling technique. For the credit card data sets, the best classification method was the k-nearest neighbor in imbalanced data, adapting the synthetic minority over sampling technique. For the wine data sets, the best method was the artificial neural network in imbalanced data adapting over sampling. Keywords: imbalanced data; k-nearest neighbor; decision tree; artificial neural network; support vector machine