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Ensemble Feature Selection for Rankings of Features
2015In the last few years, ensemble learning has been the focus of much attention mainly in classification tasks, based on the assumption that combining the output of multiple experts is better than the output of any single expert. This idea of ensemble learning can be adapted for feature selection, in which different feature selection algorithms act as ...
Borja Seijo-Pardo +3 more
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Ranked MSD: A New Feature Ranking and Feature Selection Approach for Biomarker Identification
2019In the era of big data when a huge amount of data is continuously being generated, it is common for situations to arise where the number of samples is much smaller than the number of features (variables) per sample. This phenomenon is often found in biomedical domains, where we may have relatively few patients, compared to the amount of data per ...
Ghanshyam Verma +3 more
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2011
This chapter describes a method of feature selection and ranking based on human expert knowledge and training and testing of a neural network. Being computationally efficient, the method is less sensitive to round-off errors and noise in the data than the traditional methods of feature selection and ranking grounded on the sensitivity analysis.
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This chapter describes a method of feature selection and ranking based on human expert knowledge and training and testing of a neural network. Being computationally efficient, the method is less sensitive to round-off errors and noise in the data than the traditional methods of feature selection and ranking grounded on the sensitivity analysis.
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Fast Feature Selection for Learning to Rank
Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval, 2016An emerging research area named Learning-to-Rank (LtR) has shown that effective solutions to the ranking problem can leverage machine learning techniques applied to a large set of features capturing the relevance of a candidate document for the user query. Large-scale search systems must however answer user queries very fast, and the computation of the
Gigli A +3 more
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Feature Ranking for Protein Classification
2008In this paper, a knowledge discovery framework is used for protein classification. The processing is achieved in three steps: feature extraction, feature ranking and feature selection. Inspirited from text mining results for the first step, we use n-grams descriptors; descriptors are ranked from chi-2 statistical indices in the second step; and in the ...
Faouzi Mhamdi +2 more
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Low-Rank Embedding for Robust Image Feature Extraction
IEEE Transactions on Image Processing, 2017Robustness to noises, outliers, and corruptions is an important issue in linear dimensionality reduction. Since the sample-specific corruptions and outliers exist, the class-special structure or the local geometric structure is destroyed, and thus, many existing methods, including the popular manifold learning- based linear dimensionality methods, fail
Wai Keung Wong +4 more
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Unsupervised Feature Ranking and Selection
2005Dimensionality reduction is an important issue for efficient handling of large data sets. Feature selection is effective in dimensionality reduction. Many supervised feature selection methods exist. Little work has been done for unsupervised feature ranking and selection where class information is not available.
Manoranjan Dash, Huan Liu, Jun Yao
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A Comparative Study of Feature-Salience Ranking Techniques
Neural Computation, 2001We assess the relative merits of a number of techniques designed to determine the relative salience of the elements of a feature set with respect to their ability to predict a category outcome-for example, which features of a character contribute most to accurate character recognition.
Wang, W., Jones, P., Partridge, D.
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Aggressive Feature Selection by Feature Ranking
2007Masoud Makrehchi, Mohamed Kamel
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Feature Ranking for Feature Sorting and Feature Selection with Optimisation
2023Paola Santana-Morales +6 more
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