.png){kind=icon}
.png){kind=icon}
Return
Fusion-enhanced multi-label feature selection with sparse supplementation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.inffus.2024.102813.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The exponential increase of multi-label data over various domains demands the development of effective feature selection methods. However, current sparse-learning-based feature selection methods that use LASSO- norm and 1 2 , 1-norm fail to handle two crucial issues for multi-label data. Firstly, LASSO-based methods remove features with zero-weight values during the feature selection process, some of which may have a certain degree of classification ability. Secondly, 1 2 , 1-norm-based methods may select redundant features that lead to inefficient classification results. To overcome these issues, we propose a novel sparse supplementation norm that combines inner product regularization and 1 2 , 1-norm as a novel fusion norm. This innovative fusion norm is designed to enhance the sparsity of feature selection models by leveraging the inherent row-sparse property in the 1 2 , 1-norm. Specifically, the inner product regularization norm can maintain features with potentially useful classification information, which may be discarded in traditional LASSO-based methods. At the same time, the inner product regularization norm can remove redundant features, which is introduced in traditional 1 2 , 1-norm-based methods. By incorporating this fusion norm into the Sparse-supplementation Regularized multi- label Feature Selection (SRFS) model, our method mitigates feature omission and feature redundancy, ensuring more effective and efficient feature selection for multi-label classification tasks. The experimental results on various benchmark datasets validate the efficiency and effectiveness of our proposed SRFS model.
Keywords:
Feature selection
Sparse supplementation
Multi-label learning
Norm fusion
Classification
Journal
IF:
15.5
Papers:
4.1K
Citations:
2.7W
View more organizations