剪枝网格采样的非平衡数据集分类算法..docVIP

摘 要 非平衡数据集分类问题是模式识别、机器学习和数据挖掘领域中的常见问题，也是热点问题，吸引着众多学者的眼球。非平衡数据集是指数据集类别之间存在倾斜，某一类别样本比其它类别样本要多。传统分类器为了追求高准确率，侧重于非平衡数据集中的多数类样本分类的准确性。而恰恰相反，非平衡数据集中的少数类样本往往是我们所要关心的，这时分类性能不仅要考虑分类精度高低，同时要考虑分类代价大小。传统分类器对这种非平衡数据的处理会更多关注多数类别的样本，导致大量重要的少数类别的样本错分。因此，研究非平衡数据处理问题是非常重要。 主要在数据预处理和算法两大层面上，在数据预处理层面上，学者们研究大体是对负类样本进行欠采样，去除噪声数据和远离分类面数据，对正类样本过采样，加入噪声数据以至于达到数据平衡，再采用已有分类器进行分类试图提高准确率。然而，去除数据还是加入数据，不同学者处理的方法也是不同的Abstract Imbalanced data sets classification problem is common problems in the field of pattern recognition, machine learning and data mining as well as a hot issue. Imbalanced data set is a data set of categories because of the presence of skew, namely a kind of category samples more than other categories of sample. The traditional classifiers in order to pursue a high rate of accuracy focus on classification accuracy of the majority class samples of Imbalanced data sets, on the other hand the minority class samples of imbalanced data sets should be considered because of the cost of classification and its true information.. Therefore, research of Imbalanced data processing problem is very important. At present, domestic and foreign scholars have obtained some achievements in data preprocessing and algorithms of two level about imbalanced data sets classification problem. Scholars are trying to improve the traditional algorithms and improve the classification performance in Imbalanced data set on the algorithm level. In the data pretreatment level, scholars generally remove the negative samples of noise data and separate from the classification of surface data in under-sampling, otherwise they add noise data to over-sampling data in order to balance. In a word , many methods are different on data reduction or data addition In this paper, new sampling methods about imbalanced data sets classification are considered in order to prevent the important data loss from general under-sampling method on the basis of previous studies. Grid sampling method by pruning puts forward , namel