Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285193741> ?p ?o ?g. }
Showing items 1 to 93 of
93
with 100 items per page.
- W4285193741 endingPage "55498" @default.
- W4285193741 startingPage "55488" @default.
- W4285193741 abstract "Adjusting the parameters of a machine learning algorithm can be difficult if the possible domain of expansion of these parameters is too high. In addition, if a sensible parameter is not adjusted correctly, the changes can be very impactful in the final results, making adjusting it manually not trivial. In order to adjust these features automatically, the current work proposes six models based on the use of optimization algorithms to automatically adjust the models’ parameters. These models were built around two machine learning-based algorithms, an extreme learning machine neural network and a support vector regression. The optimization algorithms used are Particle Swarm Optimization, the Artificial Bee Colony, and the genetic algorithm. The models were compared with each other based on predictive precision in the criterion of Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and statistical tests. The experimental results on ten datasets indicated that optimized algorithms models could better the performance and robustness of the non-optimized algorithms models. Therefore, the automatic adjustment of the parameters of optimized algorithms is a powerful tool to analyze different contexts of data." @default.
- W4285193741 created "2022-07-14" @default.
- W4285193741 creator A5034683327 @default.
- W4285193741 creator A5045562918 @default.
- W4285193741 creator A5047126635 @default.
- W4285193741 creator A5077974457 @default.
- W4285193741 date "2022-01-01" @default.
- W4285193741 modified "2023-09-26" @default.
- W4285193741 title "A comparison study about parameter optimization using swarm algorithms" @default.
- W4285193741 cites W141982089 @default.
- W4285193741 cites W1575995860 @default.
- W4285193741 cites W2040604977 @default.
- W4285193741 cites W2045427344 @default.
- W4285193741 cites W2047094503 @default.
- W4285193741 cites W2111072639 @default.
- W4285193741 cites W2121142820 @default.
- W4285193741 cites W2134084174 @default.
- W4285193741 cites W2150414962 @default.
- W4285193741 cites W2152195021 @default.
- W4285193741 cites W2167982865 @default.
- W4285193741 cites W2169064301 @default.
- W4285193741 cites W2560103205 @default.
- W4285193741 cites W2745573163 @default.
- W4285193741 cites W2761604870 @default.
- W4285193741 cites W2770963638 @default.
- W4285193741 cites W2803745262 @default.
- W4285193741 cites W2897313695 @default.
- W4285193741 cites W2916318597 @default.
- W4285193741 cites W2964938317 @default.
- W4285193741 cites W3040832198 @default.
- W4285193741 cites W3042582303 @default.
- W4285193741 cites W3091848785 @default.
- W4285193741 cites W3136957033 @default.
- W4285193741 cites W409166618 @default.
- W4285193741 cites W4252684946 @default.
- W4285193741 cites W4362223627 @default.
- W4285193741 doi "https://doi.org/10.1109/access.2022.3175202" @default.
- W4285193741 hasPublicationYear "2022" @default.
- W4285193741 type Work @default.
- W4285193741 citedByCount "0" @default.
- W4285193741 crossrefType "journal-article" @default.
- W4285193741 hasAuthorship W4285193741A5034683327 @default.
- W4285193741 hasAuthorship W4285193741A5045562918 @default.
- W4285193741 hasAuthorship W4285193741A5047126635 @default.
- W4285193741 hasAuthorship W4285193741A5077974457 @default.
- W4285193741 hasBestOaLocation W42851937411 @default.
- W4285193741 hasConcept C104317684 @default.
- W4285193741 hasConcept C105795698 @default.
- W4285193741 hasConcept C11413529 @default.
- W4285193741 hasConcept C119857082 @default.
- W4285193741 hasConcept C12267149 @default.
- W4285193741 hasConcept C139945424 @default.
- W4285193741 hasConcept C154945302 @default.
- W4285193741 hasConcept C185592680 @default.
- W4285193741 hasConcept C33923547 @default.
- W4285193741 hasConcept C41008148 @default.
- W4285193741 hasConcept C50644808 @default.
- W4285193741 hasConcept C55493867 @default.
- W4285193741 hasConcept C63479239 @default.
- W4285193741 hasConcept C85617194 @default.
- W4285193741 hasConceptScore W4285193741C104317684 @default.
- W4285193741 hasConceptScore W4285193741C105795698 @default.
- W4285193741 hasConceptScore W4285193741C11413529 @default.
- W4285193741 hasConceptScore W4285193741C119857082 @default.
- W4285193741 hasConceptScore W4285193741C12267149 @default.
- W4285193741 hasConceptScore W4285193741C139945424 @default.
- W4285193741 hasConceptScore W4285193741C154945302 @default.
- W4285193741 hasConceptScore W4285193741C185592680 @default.
- W4285193741 hasConceptScore W4285193741C33923547 @default.
- W4285193741 hasConceptScore W4285193741C41008148 @default.
- W4285193741 hasConceptScore W4285193741C50644808 @default.
- W4285193741 hasConceptScore W4285193741C55493867 @default.
- W4285193741 hasConceptScore W4285193741C63479239 @default.
- W4285193741 hasConceptScore W4285193741C85617194 @default.
- W4285193741 hasLocation W42851937411 @default.
- W4285193741 hasOpenAccess W4285193741 @default.
- W4285193741 hasPrimaryLocation W42851937411 @default.
- W4285193741 hasRelatedWork W1971278352 @default.
- W4285193741 hasRelatedWork W1996541855 @default.
- W4285193741 hasRelatedWork W2037316683 @default.
- W4285193741 hasRelatedWork W2355927362 @default.
- W4285193741 hasRelatedWork W2375246106 @default.
- W4285193741 hasRelatedWork W2961085424 @default.
- W4285193741 hasRelatedWork W2995227436 @default.
- W4285193741 hasRelatedWork W3115048730 @default.
- W4285193741 hasRelatedWork W3195168932 @default.
- W4285193741 hasRelatedWork W4316658362 @default.
- W4285193741 hasVolume "10" @default.
- W4285193741 isParatext "false" @default.
- W4285193741 isRetracted "false" @default.
- W4285193741 workType "article" @default.