Results 11 to 20 of about 2,058,652 (297)
Ensemble Feature Ranking [PDF]
, 2004 A crucial issue for Machine Learning and Data Mining is Feature Selection, selecting the relevant features in order to focus the learning search. A relaxed setting for Feature Selection is known as Feature Ranking, ranking the features with respect to their relevance.
Jong, Kees +4 more
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Linear feature extraction for ranking [PDF]
Information Retrieval Journal, 2018 We address the feature extraction problem for document ranking in information retrieval. We then propose LifeRank, a Linear feature extraction algorithm for Ranking. In LifeRank, we regard each document collection for ranking as a matrix, referred to as the original matrix.
Gaurav Pandey 0003 +4 more
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Hierarchical feature selection for ranking [PDF]
Proceedings of the 19th international conference on World wide web, 2010 Ranking is an essential part of information retrieval(IR) tasks such as Web search. Nowadays there are hundreds of features for ranking. So learning to rank(LTR), an interdisciplinary field of IR and machine learning(ML), has attracted increasing attention.
Guichun Hua +4 more
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A Stratified Feature Ranking Method for Supervised Feature Selection
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Most feature selection methods usually select the highest rank features which may be highly correlated with each other. In this paper, we propose a Stratified Feature Ranking (SFR) method for supervised feature selection. In the new method, a Subspace Feature Clustering (SFC) is proposed to identify feature clusters, and a stratified ...
Renjie Chen 0004 +4 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.
Boris Igelnik
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CoRR, 2023 Machine learning models are widely applied in various fields. Stakeholders often use post-hoc feature importance methods to better understand the input features' contribution to the models' predictions. The interpretation of the importance values provided by these methods is frequently based on the relative order of the features (their ranking) rather ...
Bitya Neuhof, Yuval Benjamini
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Insights into distributed feature ranking [PDF]
Information Sciences, 2019 Xunta de Galicia; ED431G ...
Verónica Bolón-Canedo +4 more
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Relational Regularization and Feature Ranking [PDF]
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014 Regularization is one of the key concepts in machine learning, but so far it has received only little attention in the logical and relational learning setting. Here we propose a regularization and feature selection technique for such setting, in which one commonly represents the structure of the domain using an entity-relationship model.
Fabrizio Costa +2 more
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Feature-ranking of the Iris dataset, according to our proposed optimally-separating ANN.
, 2021 Feature-ranking of the Iris dataset, according to our proposed optimally-separating ANN.
Yiting Tsai (4614913) +2 more
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Computers, 2021 The aim of this research study is to detect emotional state by processing electroencephalography (EEG) signals and test effect of meditation music therapy to stabilize mental state.
Nisha Vishnupant Kimmatkar +1 more
doaj +1 more source