Improve performance of extreme learning machine in classification of patchouli varieties with imbalanced class

C. Dewi, W. Mahmudy, Rio Arifando, Yoke Kusuma Arbawa, Beryl Labique Ahmadie
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引用次数: 4

Abstract

Patchouli has various varieties with almost the same physical characteristics. This often makes it difficult to recognize varieties with a high PA (Patchouli Alcohol) content. In this study an improvisation was introduced in the identification of patchouli varieties using leaf images using Extreme Learning Machine (ELM). However, problems occur in ELM if the data used is not balanced where the training process can not able to recognize data in the minority class well. Therefore, this study conducted a process to balance the composition of the data using the Synthetic Minority Over-sampling Technique (SMOTE) method. The test results of 93 data on the imbalanced composition with a comparison of 70% of training data and 30% of test data obtained an average accuracy of 93.57%. After implementing SMOTE in the Tetraploid, Patchoulina and Sidikalang classes where the amount of data in each class becomes 58 data, an average accuracy of 96.00% is achieved. This shows the existence of an increase in the process of identification with ELM when new data generation with SMOTE is carried out to balance the composition of the data.
提高极限学习机在分类不平衡广藿香品种分类中的性能
广藿香有各种各样的品种,它们的物理特性几乎相同。这通常使得很难识别PA(广藿香醇)含量高的品种。采用极限学习机(ELM)对广藿香品种的叶片图像进行了即兴鉴定。然而,如果使用的数据不平衡,训练过程不能很好地识别少数类的数据,那么在ELM中就会出现问题。因此,本研究使用合成少数派过采样技术(SMOTE)方法进行了一个平衡数据组成的过程。以70%的训练数据和30%的测试数据进行对比,对93个不平衡成分数据的测试结果,平均准确率为93.57%。在Tetraploid, Patchoulina和Sidikalang类中实现SMOTE后,每个类的数据量为58个数据,平均准确率达到96.00%。这表明,当使用SMOTE生成新数据以平衡数据的组成时,使用ELM识别的过程有所增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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