On the Scalability of Genetic Algorithms to Very Large-Scale Feature Selection

2000 ◽  
pp. 77-86 ◽  
Author(s):  
Andreas Moser ◽  
M. Narasimha Murty
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Large Scale ◽  
Scale Feature

A note on genetic algorithms for large-scale feature selection

1989 ◽  
Vol 10 (5) ◽  
pp. 335-347 ◽  
Author(s):  
W. Siedlecki ◽  
J. Sklansky
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Large Scale ◽  
Scale Feature

LARGE-SCALE FEATURE SELECTION

1993 ◽  
pp. 61-62 ◽  
Author(s):  
JACK SKLANSKY ◽  
WOJCIECH SIEDLECKI
Keyword(s):  
Feature Selection ◽  
Large Scale ◽  
Scale Feature

CCFS: A cooperating coevolution technique for large scale feature selection on microarray datasets

2018 ◽  
Vol 73 ◽  
pp. 171-178 ◽  
Author(s):  
Mohammad K. Ebrahimpour ◽  
Hossein Nezamabadi-pour ◽  
Mahdi Eftekhari
Keyword(s):  
Feature Selection ◽  
Large Scale ◽  
Scale Feature ◽  
Microarray Datasets

Parallel Large-Scale Feature Selection

2012 ◽  
pp. 352-370 ◽  
Author(s):  
Jeremy Kubica ◽  
Sameer Singh ◽  
Daria Sorokina
Keyword(s):  
Feature Selection ◽  
Large Scale ◽  
Scale Feature

scholarly journals Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection

2013 ◽  
Vol 46 ◽  
pp. 203-233 ◽  
Author(s):  
H. Zhao ◽  
X. Zhang ◽  
C. Kit
Keyword(s):  
Feature Selection ◽  
Large Scale ◽  
Critical Role ◽  
Integrative Approach ◽  
Pipeline System ◽  
Dependency Parsing ◽  
Semantic Parsing ◽  
Data Set ◽  
Scale Feature ◽  
Semantic Dependency

Semantic parsing, i.e., the automatic derivation of meaning representation such as an instantiated predicate-argument structure for a sentence, plays a critical role in deep processing of natural language. Unlike all other top systems of semantic dependency parsing that have to rely on a pipeline framework to chain up a series of submodels each specialized for a specific subtask, the one presented in this article integrates everything into one model, in hopes of achieving desirable integrity and practicality for real applications while maintaining a competitive performance. This integrative approach tackles semantic parsing as a word pair classification problem using a maximum entropy classifier. We leverage adaptive pruning of argument candidates and large-scale feature selection engineering to allow the largest feature space ever in use so far in this field, it achieves a state-of-the-art performance on the evaluation data set for CoNLL-2008 shared task, on top of all but one top pipeline system, confirming its feasibility and effectiveness.

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