A particle swarm based hybrid system for imbalanced medical data sampling.
A particle swarm based hybrid system for imbalanced medical data sampling.
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DOI：
10.1186/1471-2164-10-s3-s34
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发表时间：
2009-12-03
期刊：
影响因子：
4.4
通讯作者：
Zomaya AY
中科院分区：
文献类型：
--
作者：
Yang P;Xu L;Zhou BB;Zhang Z;Zomaya AY
医学和生物学数据通常样本量小，缺失值，最重要的是，类别分布不平衡。在这项研究中，我们提出了一种基于粒子群的混合系统来解决医学和生物数据挖掘中的类不平衡问题。该混合系统将粒子群优化(PSO)算法与多分类器和评价指标相结合进行评价融合。根据多数类样本在补偿类不平衡方面的优劣，对多数类样本进行多目标排序，然后与少数类结合形成一个均衡的数据集。这项研究的一个重要发现是，不同的分类器和度量往往会提供不同的评估结果。然而，与具有三种不同度量的几种替代方法相比，所提出的混合系统显示出一致的改进。采样结果对不同类型的分类算法也表现出较好的泛化能力，说明了信息融合在混合系统中的应用优势。实验结果表明，与现有的许多方法在不同的数据集上表现不一致不同，该混合系统具有更好的泛化特性，从而缓解了方法-数据依赖问题。从生物学角度来看，该系统为进一步调查排名较高的样本提供了指示，这可能会导致新的疾病或疾病亚型的发现。
Medical and biological data are commonly with small sample size, missing values, and most importantly, imbalanced class distribution. In this study we propose a particle swarm based hybrid system for remedying the class imbalance problem in medical and biological data mining. This hybrid system combines the particle swarm optimization (PSO) algorithm with multiple classifiers and evaluation metrics for evaluation fusion. Samples from the majority class are ranked using multiple objectives according to their merit in compensating the class imbalance, and then combined with the minority class to form a balanced dataset. One important finding of this study is that different classifiers and metrics often provide different evaluation results. Nevertheless, the proposed hybrid system demonstrates consistent improvements over several alternative methods with three different metrics. The sampling results also demonstrate good generalization on different types of classification algorithms, indicating the advantage of information fusion applied in the hybrid system. The experimental results demonstrate that unlike many currently available methods which often perform unevenly with different datasets the proposed hybrid system has a better generalization property which alleviates the method-data dependency problem. From the biological perspective, the system provides indication for further investigation of the highly ranked samples, which may result in the discovery of new conditions or disease subtypes.