Feature selection for pattern classification problems

Zhang, Li; Sun, Gang; Guo, Jun

doi:10.1109/cit.2004.1357202

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…There is a considerable amount of literature about feature selection [14,15,20,24,25] for supervised classification, an excellent survey about feature selection algorithms can be found in [11]. In this section, we only present some ranking based methods for supervised feature selection, which are some of the most used in the last years of feature selection for Pattern Recognition and Machine Learning [18].…”

Section: Feature Selection Techniques In Supervised Classificationmentioning

confidence: 99%

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

Solorio-Fernández

Carrasco-Ochoa

Martínez-Trinidad

2010

Advances in Pattern Recognition

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Abstract. In this paper, we introduce a new hybrid filter-wrapper method for supervised feature selection, based on the Laplacian Score ranking combined with a wrapper strategy. We propose to rank features with the Laplacian Score to reduce the search space, and then we use this order to find the best feature subset. We compare our method against other based on ranking feature selection methods, namely, Information Gain Attribute Ranking, Relief, Correlation-based Feature Selection, and additionally we include in our comparison a Wrapper Subset Evaluation method. Empirical results over ten real-world datasets from the UCI repository show that our hybrid method is competitive and outperforms in most of the cases to the other feature selection methods used in our experiments.

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