Results 251 to 260 of about 102,167 (290)

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, Xiaojun Chen 0006, Guowen Yuan, Wenya Sun, Qingyao Wu   +4 more
openaire   +2 more sources

Feature Selection and Ranking

, 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
openaire   +2 more sources

Some of the next articles are maybe not open access.

Related searches:

Feature selection for ranking

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, Tie-Yan Liu, Tao Qin 0001, Hang Li 0001   +3 more
openaire   +1 more source

Speeding up Document Ranking with Rank-based Features

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015
Learning 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, Nardini F M, Orlando S, Perego R, Tonellotto N   +4 more
openaire   +2 more sources

Greedy feature selection for ranking

Proceedings of the 2011 15th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2011
This 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, Yong Tang 0001, Hai-Xia Luo, Yan Pan 0002   +3 more
openaire   +1 more source

Ensemble Feature Selection for Rankings of Features

2015
In 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, Verónica Bolón-Canedo, Iago Porto-Díaz, Amparo Alonso-Betanzos   +3 more
openaire   +1 more source

Feature Ranking for Protein Classification

2008
In 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, Ricco Rakotomalala, Mourad Elloumi   +2 more
openaire   +1 more source

Feature Ranking Computation Algorithm

International Journal of Organizational and Collective Intelligence, 2012
This 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.
openaire   +1 more source

Fast Feature Selection for Learning to Rank

Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval, 2016
An 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, Lucchese C, Nardini FM, Perego R   +3 more
openaire   +2 more sources

Learning to Rank with Labeled Features

Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval, 2016
Classic 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 ...
openaire   +1 more source