[1]HU Minjie,LIN Yaojin,YANG Honghe,et al.Spectral feature selection based on feature correlation[J].CAAI Transactions on Intelligent Systems,2017,12(4):519-525.[doi:10.11992/tis.201609008]
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Spectral feature selection based on feature correlation
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 4
Page number:
519-525
Column:
学术论文—机器学习
Public date:
2017-08-25
- Title:
- Spectral feature selection based on feature correlation
- Keywords:
- feature selection; spectral feature selection; spectral graph theory; feature relevance; discernibility; search strategy; Laplacian score; classification performance
- CLC:
- TP18
- DOI:
- 10.11992/tis.201609008
- Abstract:
- In the traditional spectrum feature selection algorithm, only the importance of single features are considered. In this paper, we introduce the statistical correlation between features into traditional spectrum analysis and construct a spectral feature selection model based on feature correlation. First, the proposed model utilizes the Laplacian Score to identify the most central feature as the selected feature, then designs a new feature group discernibility objective function, and applies the forward greedy search strategy to sequentially evaluate the candidate features. Then, the candidate feature with the minimum objective function is added to the selected features. The algorithm considers both the importance of feature as well as the correlations between features. We conducted experiments on two different classifiers and eight UCI datasets, the results of which show that the algorithm effectively improves the classification performance of the feature subset and also obtains a small number of feature subsets with high classification precision.
- References:
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Last Update:
2017-08-25