Feature selection method for high-dimensional big data analysis and computer storage medium
A feature selection method and data analysis technology, applied in computer components, calculations, instruments, etc., can solve problems such as inability to remove redundant features, high-dimensional big data analysis dimension disaster, etc., and achieve good removal of redundant and irrelevant features Effect
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[0046] The method of the invention is a feature selection method for high-dimensional big data analysis. First, the correlation between features and categories is calculated, the features are sorted, irrelevant features are removed, and then the features are sorted according to the correlation between the remaining features. Clustering, and finally select the representative features of each feature cluster to remove some redundant features. In the field of software defect prediction, software defect datasets generally have the characteristics of large data volume and high dimension. The method of the present invention takes the software defect prediction data set pc4 as an example. According to the correlation between features and categories, the features are sorted, irrelevant features are removed, and then the features are clustered, and the representative features in the feature clusters are selected to remove some redundant features and construct the final feature subset. ...
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