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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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Feature Ranking Computation Algorithm
International Journal of Organizational and Collective Intelligence, 2012This journal paper describes an algorithm of feature ranking computation, based both on a data set with a potentially excessive number of features and a neural network trained and tested on this set. Each member of the data set contains many features (inputs) and one output.
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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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Learning to Rank with Labeled Features
Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval, 2016Classic learning to rank algorithms are trained using a set of labeled documents, pairs of documents, or rankings of documents. Unfortunately, in many situations, gathering such labels requires significant overhead in terms of time and money. We present an algorithm for training a learning to rank model using a set of labeled features elicited from ...
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Social Ranking for Feature Selection
International Joint Conference on Autonomous Agents and Multiagent SystemsIn this paper, we focus on limitations in the use of the Shapley value within the field of eXplainable AI (XAI) through the lens of the axiomatic analysis and its implications in the realm of machine learning. As an alternative to the Shapley value, we analyse the properties of the lex-cel, a social ranking solution introduced inthe recent literature ...
Laurent Gourvès +2 more
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Ranking a random feature for variable and feature selection
J. Mach. Learn. Res., 2003Summary: We describe a feature selection method that can be applied directly to models that are linear with respect to their parameters, and indirectly to others. It is independent of the target machine. It is closely related to classical statistical hypothesis tests, but it is more intuitive, hence more suitable for use by engineers who are not ...
Hervé Stoppiglia +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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Feature Ranking Based on Decision Border
2010 20th International Conference on Pattern Recognition, 2010In this paper a Feature Ranking algorithm for classification is proposed, which is based on the notion of Bayes decision border. The method elaborates upon the results of the Decision Border Feature Extraction approach, exploiting properties of eigenvalues and eigenvectors of the orthogonal transformation to calculate the discriminative importance ...
Claudia Diamantini +2 more
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AI Commun., 2003
Summary: The paper proposes a novel feature ranking technique using discernibility matrix. Discernibility matrix is used in rough set theory for reduct computation. By making use of attribute frequency information in discernibility matrix, the paper develops a fast feature ranking mechanism.
Keyun Hu, Yuchang Lu, Chunyi Shi
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Summary: The paper proposes a novel feature ranking technique using discernibility matrix. Discernibility matrix is used in rough set theory for reduct computation. By making use of attribute frequency information in discernibility matrix, the paper develops a fast feature ranking mechanism.
Keyun Hu, Yuchang Lu, Chunyi Shi
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Combining feature ranking for text classification
2007 IEEE International Conference on Systems, Man and Cybernetics, 2007Feature ranking is one of the dimensionality reduction methods. Because of its simplicity and low cost, it is widely used in text classification. One problem with feature ranking methods is their non-robust behavior when applied to different data sets. In other words, the feature ranking methods behave differently from one data set to the other.
Masoud Makrehchi, Mohamed S. Kamel
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