Hybrid Feature Selection Method for Supervised Classification Based on Laplacian Score Ranking

Saúl Solorio-Fernández; Jesús Ariel Carrasco-Ochoa; José Fco. Martínez-Trinidad

doi:10.1007/978-3-642-15992-3_28

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Hybrid Feature Selection Method for Supervised Classification Based on Laplacian Score Ranking

Saúl Solorio-Fernández Jesús Ariel Carrasco-Ochoa José Fco. Martínez-Trinidad

Year: 2010 Lecture notes in computer science Pages: 260-269 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-642-15992-3_28

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Keywords:

Feature selection Ranking (information retrieval) Computer science Artificial intelligence Pattern recognition (psychology) Rank (graph theory) Selection (genetic algorithm) Feature (linguistics) Data mining Machine learning Ranking SVM Mathematics

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Text and Document Classification Technologies

Physical Sciences → Computer Science → Artificial Intelligence

Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Machine Learning and Data Classification

Physical Sciences → Computer Science → Artificial Intelligence

Hybrid Feature Selection Method for Supervised Classification Based on Laplacian Score Ranking

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