论文标题
多样性感知的加权多数投票分类器不平衡数据
Diversity-Aware Weighted Majority Vote Classifier for Imbalanced Data
论文作者
论文摘要
在本文中，我们提出了一种基于多样性的集合学习算法（称为DAMVI）来处理不平衡的算法分类任务。具体而言，在学习了基础分类器之后，算法i）增加了积极的例子（少数族裔类）的权重，这些示例（少数族裔类）“很难”用均匀加权的基本分类器进行分类； ii）然后通过优化PAC-Bayesian c-bound来了解基础分类器的权重，该C型考虑分类器之间的准确性和多样性。我们在预测维护任务，信用卡欺诈检测，网页分类和医疗应用程序方面展示了拟议方法的效率。
In this paper, we propose a diversity-aware ensemble learning based algorithm, referred to as DAMVI, to deal with imbalanced binary classification tasks. Specifically, after learning base classifiers, the algorithm i) increases the weights of positive examples (minority class) which are "hard" to classify with uniformly weighted base classifiers; and ii) then learns weights over base classifiers by optimizing the PAC-Bayesian C-Bound that takes into account the accuracy and diversity between the classifiers. We show efficiency of the proposed approach with respect to state-of-art models on predictive maintenance task, credit card fraud detection, webpage classification and medical applications.
