We design a new adaptive learning algorithm for misclassification cost problems that attempt to reduce the cost of misclassified instances derived from the consequences of various errors. Our algorithm (adaptive cost sensitive learning - AdaCSL) adaptively adjusts the loss function such that the classifier bridges the difference between the class distributions between subgroups of samples in the training and test data sets with similar predicted probabilities (i.e., local training-test class distribution mismatch). We provide some theoretical performance guarantees on the proposed algorithm and present empirical evidence that a deep neural network used with the proposed AdaCSL algorithm yields better cost results on several binary classification data sets that have class-imbalanced and class-balanced distributions compared to other alternative approaches.
翻译:我们为分类错误的成本问题设计了新的适应性学习算法,试图降低因各种错误的后果而导致的错误分类错误的费用。我们的算法(适应性成本敏感学习-AdaCSL)适应性地调整了损失功能,这样分类者就能弥合培训和测试数据集中样本分组之间的等级分布差异,并得出类似的预测概率(即当地培训测试等级分配不匹配 ) 。 我们为拟议的算法提供了一些理论性能保障,并提供了经验证据,证明与拟议的AdaCSL算法一起使用的深层神经网络在几个二元分类数据集中产生了更好的成本结果,这些数据集的等级分布与其他替代方法相比是平衡和平衡的。