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Estimating Life Expectancy in the U.S.: Identifying Environmental Determinants and Disparities (2003 to 2019). [PDF]
Sensors (Basel)Ruwali S, Lary D, Shrestha S, Ahmad F.
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Elastic Ranking-Driven Time Series Feature Optimization for Aeroengine Remaining Useful Life Prediction. [PDF]
Entropy (Basel)Xin R, Bai S, Fan J, Gao C, Feng X.
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Combining Feature Ranking Algorithms Through Rank Aggregation [PDF]
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012 The problem of combining multiple feature rankings into a more robust ranking is investigated. A general framework for ensemble feature ranking is proposed, alongside four instantiations of this framework using different ranking aggregation methods. An empirical evaluation using 39 UCI datasets, three different learning algorithms and three different ...
Ronaldo C Prati
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Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007
Ranking is a very important topic in information retrieval. While algorithms for learning ranking models have been intensively studied, this is not the case for feature selection, despite of its importance. The reality is that many feature selection methods used in classification are directly applied to ranking.
Xiubo Geng +3 more
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Ranking is a very important topic in information retrieval. While algorithms for learning ranking models have been intensively studied, this is not the case for feature selection, despite of its importance. The reality is that many feature selection methods used in classification are directly applied to ranking.
Xiubo Geng +3 more
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Speeding up Document Ranking with Rank-based Features
Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015Learning to Rank (LtR) is an effective machine learning me- thodology for inducing high-quality document ranking func- tions. Given a query and a candidate set of documents, where query-document pairs are represented by feature vec- tors, a machine-learned function is used to reorder this set.
Lucchese C +4 more
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Greedy feature selection for ranking
Proceedings of the 2011 15th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2011This paper is concerned with a study on the feature selection for ranking. Learning to rank is a useful tool for collaborative filtering and many other collaborative systems, which many algorithms have been proposed for dealing this issue. But feature selection methods receive little attention, despite of their importance in collaborative filtering ...
Hanjiang Lai +3 more
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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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