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首页> 外文会议>International Conference on Informatics and Computational Sciences >The Study of Synthetic Minority Over-sampling Technique (SMOTE) and Weighted Extreme Learning Machine for Handling Imbalance Problem on Multiclass Microarray classification
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The Study of Synthetic Minority Over-sampling Technique (SMOTE) and Weighted Extreme Learning Machine for Handling Imbalance Problem on Multiclass Microarray classification

机译：综合少数族群过采样技术（SMOTE）和加权极限学习机处理多类微阵列分类失衡问题的研究

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摘要

Microarray data classification has a great challenge due to number of samples which is much smaller compared to the number of genes. The problem is getting harder when the dataset has multiclass target and the number of samples in each class is not well distributed (which is called imbalance data distribution). In this research, two different approaches to handle imbalance data distribution are studied, they are SMOTE (based on data approach) and weighted ELM (based on algorithmic approach). To evaluate the performance of the proposed method, two public imbalanced multiclass microarray dataset are used, GCM (Global Cancer Map) and Subtypes-Leukemia dataset. The results of experiment show that the implementation of SMOTE and weighted ELM on GCM dataset have no significant effect in the classification performance. Different with the Subtypes-Leukemia dataset, the implementation of SMOTE and weighted ELM has improved the classification performance compared to the previous research. Generally, the results show that weighted ELM perform slightly better compared to SMOTE to increase the accuracy of the minority class.
机译：由于样品数量比基因数量小得多，因此微阵列数据分类面临巨大挑战。当数据集具有多类目标并且每个类中的样本数分布不均时（称为不平衡数据分布），问题变得更加棘手。在这项研究中，研究了两种不同的处理不平衡数据分布的方法，它们是SMOTE（基于数据方法）和加权ELM（基于算法方法）。为了评估所提出方法的性能，使用了两个公共不平衡多类微阵列数据集，即GCM（全球癌症图）和Subtypes-Leukemia数据集。实验结果表明，在GCM数据集上实施SMOTE和加权ELM对分类性能没有显着影响。与亚型白血病数据集不同，与以前的研究相比，SMOTE和加权ELM的实施提高了分类性能。通常，结果表明，加权ELM的效果比SMOTE略好，以提高少数派类别的准确性。

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