SGFS: A semi-supervised graph-based feature selection algorithm based on the PageRank algorithm
Author:
Affiliation:
1. Lorestan University,Department of Computer Engineering,KhorramAbad,Iran
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9780464/9780394/09780486.pdf?arnumber=9780486
Reference35 articles.
1. Semi-supervised sparse feature selection via graph Laplacian based scatter matrix for regression problems
2. Semi-Supervised Non-Negative Matrix Factorization With Dissimilarity and Similarity Regularization
3. Semi-supervised local Fisher discriminant analysis for dimensionality reduction
4. Simple strategies for semi-supervised feature selection
5. Evaluation of Ensembles for Feature Selection
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1. Multi-objective Optimization for Feature Selection: A Review;Springer Tracts in Nature-Inspired Computing;2024
2. Exploring Ant Colony Optimization for Feature Selection: A Comprehensive Review;Springer Tracts in Nature-Inspired Computing;2024
3. SemiACO: A semi-supervised feature selection based on ant colony optimization;Expert Systems with Applications;2023-03
4. A Fuzzy Integral Approach for Ensembling Unsupervised Feature Selection Algorithms;2023 28th International Computer Conference, Computer Society of Iran (CSICC);2023-01-25
5. Ensemble Feature Selection for Multi-label Classification: A Rank Aggregation Method;Lecture Notes in Networks and Systems;2023
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