Results 261 to 270 of about 796,511 (318)
Statistically principled feature selection for single cell transcriptomics. [PDF]
BMC BioinformaticsDollinger EP +4 more
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Learning With Selected Features
IEEE Transactions on Cybernetics, 2022The coming big data era brings data of unprecedented size and launches an innovation of learning algorithms in statistical and machine-learning communities. The classical kernel-based regularized least-squares (RLS) algorithm is excluded in the innovation, due to its computational and storage bottlenecks.
Shao-Bo Lin +2 more
openaire +2 more sources
Feature Selection for Classification
Intelligent Data Analysis, 1997Feature selection has been the focus of interest for quite some time and much work has been done. With the creation of huge databases and the consequent requirements for good machine learning techniques, new problems arise and novel approaches to feature selection are in demand.
Dash, M., Liu, H.
openaire +1 more source
Parallelizing Feature Selection
Algorithmica, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jerffeson Teixeira de Souza +2 more
openaire +2 more sources
2015 IEEE International Conference on Data Mining Workshop (ICDMW), 2015
This work introduces adversarial feature selection, a game between a feature selection agent and its adversary. The adversarial approach is drawn from existing work on adversarial classification. The feature selection algorithm selects a subset of features from the original set based on their utility towards classification accuracy.
Karan Kumar Budhraja, Tim Oates 0001
openaire +1 more source
This work introduces adversarial feature selection, a game between a feature selection agent and its adversary. The adversarial approach is drawn from existing work on adversarial classification. The feature selection algorithm selects a subset of features from the original set based on their utility towards classification accuracy.
Karan Kumar Budhraja, Tim Oates 0001
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
IEEE Intelligent Systems, 2005
Data preprocessing is an indispensable step in effective data analysis. It prepares data for data mining and machine learning, which aim to turn data into business intelligence or knowledge. Feature selection is a preprocessing technique commonly used on high-dimensional data.
Huan Liu 0001 +12 more
openaire +1 more source
Data preprocessing is an indispensable step in effective data analysis. It prepares data for data mining and machine learning, which aim to turn data into business intelligence or knowledge. Feature selection is a preprocessing technique commonly used on high-dimensional data.
Huan Liu 0001 +12 more
openaire +1 more source