Results 261 to 270 of about 102,167 (290)
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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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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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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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Learning to rank with (a lot of) word features
Information Retrieval, 2009In this article we present Supervised Semantic Indexing which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy ...
Bing Bai +7 more
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Fusion in multi-criterion feature ranking
2007 10th International Conference on Information Fusion, 2007Feature ranking, due to its simplicity and computational efficiency, is a widely used dimensionality reduction technique, especially for large dataset where other methods are computationally too expensive. Conventionally feature ranking is done based on a single ranking criterion.
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2011
This chapter introduces a feature-based retrieval model based on Markov random fields (MRF model), which serves as the primary retrieval model throughout the remainder of the book. Although there are many different ways to formulate a general feature-based model for information retrieval, this work focuses on the MRF model because it satisfies the ...
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This chapter introduces a feature-based retrieval model based on Markov random fields (MRF model), which serves as the primary retrieval model throughout the remainder of the book. Although there are many different ways to formulate a general feature-based model for information retrieval, this work focuses on the MRF model because it satisfies the ...
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USING STATISTICAL MEASURES FOR FEATURE RANKING
International Journal of Pattern Recognition and Artificial Intelligence, 2013Feature ranking is a fundamental preprocess for feature selection, before performing any data mining task. Essentially, when there are too many features in the problem, dimensionality reduction through discarding weak features is highly desirable. In this paper, we have developed an efficient feature ranking algorithm for selecting the more relevant ...
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