关键词: 不平衡数据集, 阈值判定标准, 神经网络, 遗传算法

Abstract: The classification of imbalance datasets is a hot research area in the field of machine learning，and recently，many researchers have proposed several theories and algorithms to improve the performance of classical classification algorithms on imbalance datasets.One of the most important methods is adopting threshold selection criteria to determine the output threshold of an Artificial Neural Network（ANN）.The commonly used threshold selection criteria have some drawbacks，such as failing to get optimal classification performances both on data in minority class and in majority class，only focusing on the classification accuracy of the majority class data.This paper proposes a new threshold selection criterion based on which，both the data in the minority class and majority class can reach optimal classification accuracies without the impact of the sample proportion.When the new threshold selection criterion is applied as a classifier evaluation criterion to classifiers trained using Artificial Neural Networks and Genetic approaches，good results can be obtained.

Key words: imbalance datasets, threshold selection criterion, Artificial Neural Network（ANN）, genetic method