A Balanced Accuracy Fitness Function Leads to Robust Analysis using Grammatical Evolution Neural Networks in the Case of Class Imbalance.
A Balanced Accuracy Fitness Function Leads to Robust Analysis using Grammatical Evolution Neural Networks in the Case of Class Imbalance.
复制标题
在类不平衡的情况下，平衡的准确性适应度函数可以使用语法进化神经网络进行稳健的分析。
DOI：
10.1145/1389095.1389159
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Motsinger-Reif,AlisonA
中科院分区：
文献类型：
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作者：
Hardison,NicholasE;Fanelli,TheresaJ;Dudek,ScottM;Reif,DavidM;Ritchie,MarylynD;Motsinger-Reif,AlisonA
语法进化神经网络（GENN）是一种计算方法，旨在检测遗传流行病学中基因间的相互作用，但迄今为止仅在病例和对照数量平衡的情况下进行了评估。然而，真实数据很少有如此完美平衡的类别。在当前的研究中，我们测试了 GENN 使用两个适应度函数（分类误差和平衡误差）以及数据重新采样来检测具有一系列类别不平衡的数据中的交互的能力。我们表明，当使用分类误差时，类别不平衡会大大降低 GENN 的功效。重新采样方法证明功率有所提高，但使用平衡精度会产生最高功率。根据本研究的结果，GENN算法中平衡误差取代了分类误差
Grammatical Evolution Neural Networks (GENN) is a computational method designed to detect gene-gene interactions in genetic epidemiology, but has so far only been evaluated in situations with balanced numbers of cases and controls. Real data, however, rarely has such perfectly balanced classes. In the current study, we test the power of GENN to detect interactions in data with a range of class imbalance using two fitness functions (classification error and balanced error), as well as data re-sampling. We show that when using classification error, class imbalance greatly decreases the power of GENN. Re-sampling methods demonstrated improved power, but using balanced accuracy resulted in the highest power. Based on the results of this study, balanced error has replaced classification error in the GENN algorithm