Two-Stage Discriminative Feature Selection
| Autor: | Shaoru Wu, Xiaobin Zhi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
business.industry Computer science Dimensionality reduction Feature selection Pattern recognition 02 engineering and technology Linear discriminant analysis Regularization (mathematics) 03 medical and health sciences 030104 developmental biology Discriminative model 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Eigendecomposition of a matrix |
| Zdroj: | Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery ISBN: 9783030325909 ICNC-FSKD |
| Popis: | Recently, a filter supervised feature selection method, namely discriminative feature selection (DFS), was proposed, which combines linear discriminant analysis (LDA) and sparsity regularization effectively. However, DFS is computationally expensive due to the use of eigenvalue decomposition on large matrix. In this paper, we propose a two-stage DFS method, namely TSDFS, to improve the efficiency and keep the accuracy of classification. A direct LDA based feature selection is firstly performed to achieve dimension reduction preprocessing of the data. Then, a DFS procedure is performed efficiently on the reduced data in the second stage. The high efficiency of the whole TSDFS is credited with the high efficiency of dimension reduction preprocessing. In addition, we employ nested cross-validation technology to achieve automatic parameter selection for TSDFS. Extensive experimental results based on several benchmark data sets validate the effectiveness of TSDFS. |
| Databáze: | OpenAIRE |
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