CAJ | 학술논문

　　不平衡数据分类是模式分类领域较难处理的一类问题，其主要原因在于类间样本数目不均衡。为了有效地提高不平衡数据分类效果，本文提出了一种引入偏置选择变量的不平衡数据集重采样算法。该算法引入一个偏置选择变量，该变量定义了多数类样本被取样的概率。通过引入偏置选择变量可以有效地降低不平衡度，因此能很好地提高分类算法在不平衡数据集上的泛化性能。在人工生成数据集上的分类实验充分验证了本文重采样算法的有效性。
　　불평형수거분류시모식분류영역교난처리적일류문제，기주요원인재우류간양본수목불균형。위료유효지제고불평형수거분류효과，본문제출료일충인입편치선택변량적불평형수거집중채양산법。해산법인입일개편치선택변량，해변량정의료다수류양본피취양적개솔。통과인입편치선택변량가이유효지강저불평형도，인차능흔호지제고분류산법재불평형수거집상적범화성능。재인공생성수거집상적분류실험충분험증료본문중채양산법적유효성。
Imbalanced data classification is more difficult to handle in the field of pattern classification, mainly due to the uneven number of samples between classes. In order to effectively improve the classification performance on imbalanced data set, this paper proposes an imbalanced data set resampling algorithm by introducing bias selection variable. The al-gorithm introduced a bias selection variable, which defines the sampling probability of the majority class sample. By in-troducing bias selection variables, the imbalanced degree of data sets can be effectively reduced, and thus the generaliza-tion performance of the classification algorithm on imbalanced data sets can be improved . Classification experiments on artificially generated data sets fully verify the validity of this proposed algorithm.