Results 91 to 100 of about 2,058,652 (297)
Feature importance ranking when performing fastEmbed model on NVD.
, 2020 Feature importance ranking when performing fastEmbed model on NVD.
Yong Fang (301344) +3 more
core +1 more source
Molecular Oncology, EarlyView.This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska +13 more
wiley +1 more source
Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality
CoRR, 2017 In an era where accumulating data is easy and storing it inexpensive, feature selection plays a central role in helping to reduce the high-dimensionality of huge amounts of otherwise meaningless data. In this paper, we propose a graph-based method for feature selection that ranks features by identifying the most important ones into arbitrary set of ...
Roffo Giorgio, Melzi Simone
openaire +3 more sources
LLM feature ranking for the impaired QoL class in the three cases. (A): all group; (B): controlled asthma group; (C): Not controlled asthma group.
Maurizio Mongelli (14134888) +3 more
core +1 more source
Feature importance ranking when performing fastEmbed model on SecurityFocus.
, 2020 Feature importance ranking when performing fastEmbed model on SecurityFocus.
Yong Fang (301344) +3 more
core +1 more source
Molecular Oncology, EarlyView.Combining osimertinib with the STING agonist ADU‐S100 activates innate and adaptive immunity to overcome the non‐inflamed microenvironment of Egfr‐mutant lung cancer. This combination increases NK and CD8+ T‐cell infiltration, associated with activation of the STING‐IRF3 pathway and local immunogenic cell death.
Jun Nishimura +19 more
wiley +1 more source
Feature Ranking for Total Ordering Ranking Problems
Proceedings of the 2010 International Conference on E-Business Intelligence, 2010 In this paper, we first introduce the development of learning to rank and discuss the problems existing in this field especially the ignorance of total ordering ranking. For dealing with the total ordering ranking problem, we assume a method “feature ranking”. Based the assumption, we design two algorithms: Feature Rank and BL-FeatureRank.
Daniel Zeng, Yongqing Wang
openaire +2 more sources
Feature-conscious ranking framework
, 2007 The PageRank algorithm [1] for Web search ranking models the Internet as a content-agnostic link graph, and derives the importance of individual Web pages without regard to their content or other properties.
Xu, Kaijian
core
Efficient Feature Weighting Methods for Ranking
, 2009 Feature weighting or selection is a crucial process to identify an important subset of features from a data set. Removing irrelevant or redundant features can improve the generalization performance of ranking functions in information retrieval.
Yu, Hwanjo +5 more
core +1 more source
Using the quantum probability ranking principle to rank interdependent documents [PDF]
, 2010 A known limitation of the Probability Ranking Principle (PRP) is that it does not cater for dependence between documents. Recently, the Quantum Probability Ranking Principle (QPRP) has been proposed, which implicitly captures dependencies between ...
Azzopardi, L. +5 more
core +2 more sources