论文标题
ASTRA：一种新颖的算法级别的分类方法
ASTra: A Novel Algorithm-Level Approach to Imbalanced Classification
论文作者
论文摘要
我们提出了一种新型的输出层激活函数，我们将其命名为ASTRA（不对称的Sigmoid转移函数），该功能使少数群体的分类在高度不平衡的情况下，更可行。我们将其与损失功能相结合，有助于有效地针对少数族裔错误分类。这两种方法可以一起使用，也可以分别使用，建议将其组合用于最严重不平衡的情况。提出的方法在IRS上进行了588.24至4000的数据集测试，很少有少数示例（在某些数据集中，只有5个）。在最近的一项部署了广泛的复杂，混合数据级的集体分类器中，使用两个至12个隐藏单元的神经网络与两个隐藏单元相当或比等效的结果相当或更好。
We propose a novel output layer activation function, which we name ASTra (Asymmetric Sigmoid Transfer function), which makes the classification of minority examples, in scenarios of high imbalance, more tractable. We combine this with a loss function that helps to effectively target minority misclassification. These two methods can be used together or separately, with their combination recommended for the most severely imbalanced cases. The proposed approach is tested on datasets with IRs from 588.24 to 4000 and very few minority examples (in some datasets, as few as five). Results using neural networks with from two to 12 hidden units are demonstrated to be comparable to, or better than, equivalent results obtained in a recent study that deployed a wide range of complex, hybrid data-level ensemble classifiers.
