Research Article
Efficient Feature Selection Algorithm for High-Dimensional Non-equilibrium Big Data Set
@INPROCEEDINGS{10.1007/978-3-030-67871-5_36, author={Shuang-cheng Jia and Feng-ping Yang}, title={Efficient Feature Selection Algorithm for High-Dimensional Non-equilibrium Big Data Set}, proceedings={Advanced Hybrid Information Processing. 4th EAI International Conference, ADHIP 2020, Binzhou, China, September 26-27, 2020, Proceedings, Part I}, proceedings_a={ADHIP}, year={2021}, month={2}, keywords={High dimensional data Non-equilibrium feature Granulation fusion Feature selection}, doi={10.1007/978-3-030-67871-5_36} }- Shuang-cheng Jia
Feng-ping Yang
Year: 2021
Efficient Feature Selection Algorithm for High-Dimensional Non-equilibrium Big Data Set
ADHIP
Springer
DOI: 10.1007/978-3-030-67871-5_36
Abstract
When the traditional algorithm is used to calculate the feature classification of high-dimensional non-equilibrium and large data set, it is easy to appear the problem of low accuracy and recall rate of feature selection. Therefore, a feature selection algorithm based on granular fusion is designed. By using the regularization feature of the data, the original big data aggregate is transformed into a small-scale data subset. On the basis of this, the feature selection function of the data particle is obtained. Finally, the weight fusion calculation of each feature subset is carried out. The feature classification of high-dimensional non-equilibrium big data set is realized. The experimental results show that the feature selection algorithm based on granular fusion can realize the feature selection and recall of high dimensional unbalanced data sets. The accuracy of the method is higher than that of the traditional method, which shows that the method is feasible and effective.
