Data classification forecasting method based on neighborhood rough set and PCA fusion

The invention provides a data classification forecasting method based on neighborhood rough set and PCA fusion. The method includes collecting sample data to form a sample data set; calculating the neighborhood rough set weight vector and the principal component weight vector according to the sample data set; and fusing the neighborhood rough set weight vector and the principal component weight vector to obtain the fused weight vector, and classifying and predicting the data. The data classification forecasting method based on neighborhood rough set and PCA fusion can effectively solve the problem of insufficient ability of existing supervised learning and unsupervised learning data classification and processing by fusing the neighborhood rough set weight and the PCA weight, and provides the basis for the computer data processing system to be able to dig out more valuable knowledge by making data decision making evaluation of the sample data set. 本发明提供种基于邻域粗糙集和PCA融合的数据分类预测方法，包括采集样本数据，形成样本数据集；根据样本数据集，计算邻域粗糙集权重