Matches in SemOpenAlex for { <https://semopenalex.org/work/W2907502017> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W2907502017 abstract "Online news is a media for people to get new information. There are a lot of online news media out there and a many people will only read news that is interesting for them. This kind of news tends to be popular and will bring profit to the media owner. That’s why, it is necessary to predict whether a news is popular or not by using the prediction methods. Machine learning is one of the popular prediction methods we can use. In order to make a higher accuracy of prediction, the best hyper parameter of machine learning methods need to be determined. Determining the hyper parameter can be time consuming if we use grid search method because grid search is a method which tries all possible combination of hyper parameter. This is a problem because we need a quicker time to make a prediction of online news popularity. Hence, genetic algorithm is proposed as the alternative solution because genetic algorithm can get optimal hypermeter with reasonable time. The result of implementation shows that genetic algorithm can get the hyper parameter with almost the same result with grid search with faster computational time. The reduction in computational time is as follows: Support Vector Machine is 425.06%, Random forest is 17%, Adaptive Boosting is 651.06%, and lastly K - Nearest Neighbour is 396.72%." @default.
- W2907502017 created "2019-01-11" @default.
- W2907502017 creator A5020764575 @default.
- W2907502017 creator A5042145849 @default.
- W2907502017 date "2018-01-01" @default.
- W2907502017 modified "2023-10-01" @default.
- W2907502017 title "Hyper Parameter Optimization using Genetic Algorithm on Machine Learning Methods for Online News Popularity Prediction" @default.
- W2907502017 cites W1633216914 @default.
- W2907502017 cites W1788633279 @default.
- W2907502017 cites W1863734467 @default.
- W2907502017 cites W1970957223 @default.
- W2907502017 cites W2062261051 @default.
- W2907502017 cites W2086004682 @default.
- W2907502017 cites W2087016914 @default.
- W2907502017 cites W2181725657 @default.
- W2907502017 cites W2297696965 @default.
- W2907502017 cites W2316290550 @default.
- W2907502017 cites W2403156809 @default.
- W2907502017 cites W2565261883 @default.
- W2907502017 cites W2745098819 @default.
- W2907502017 cites W2763495894 @default.
- W2907502017 cites W3200665312 @default.
- W2907502017 doi "https://doi.org/10.14569/ijacsa.2018.091238" @default.
- W2907502017 hasPublicationYear "2018" @default.
- W2907502017 type Work @default.
- W2907502017 sameAs 2907502017 @default.
- W2907502017 citedByCount "23" @default.
- W2907502017 countsByYear W29075020172019 @default.
- W2907502017 countsByYear W29075020172020 @default.
- W2907502017 countsByYear W29075020172021 @default.
- W2907502017 countsByYear W29075020172022 @default.
- W2907502017 countsByYear W29075020172023 @default.
- W2907502017 crossrefType "journal-article" @default.
- W2907502017 hasAuthorship W2907502017A5020764575 @default.
- W2907502017 hasAuthorship W2907502017A5042145849 @default.
- W2907502017 hasBestOaLocation W29075020171 @default.
- W2907502017 hasConcept C10485038 @default.
- W2907502017 hasConcept C11413529 @default.
- W2907502017 hasConcept C119857082 @default.
- W2907502017 hasConcept C12267149 @default.
- W2907502017 hasConcept C124101348 @default.
- W2907502017 hasConcept C154945302 @default.
- W2907502017 hasConcept C15744967 @default.
- W2907502017 hasConcept C187691185 @default.
- W2907502017 hasConcept C196921405 @default.
- W2907502017 hasConcept C2524010 @default.
- W2907502017 hasConcept C2780586970 @default.
- W2907502017 hasConcept C33923547 @default.
- W2907502017 hasConcept C41008148 @default.
- W2907502017 hasConcept C46686674 @default.
- W2907502017 hasConcept C77805123 @default.
- W2907502017 hasConcept C8880873 @default.
- W2907502017 hasConceptScore W2907502017C10485038 @default.
- W2907502017 hasConceptScore W2907502017C11413529 @default.
- W2907502017 hasConceptScore W2907502017C119857082 @default.
- W2907502017 hasConceptScore W2907502017C12267149 @default.
- W2907502017 hasConceptScore W2907502017C124101348 @default.
- W2907502017 hasConceptScore W2907502017C154945302 @default.
- W2907502017 hasConceptScore W2907502017C15744967 @default.
- W2907502017 hasConceptScore W2907502017C187691185 @default.
- W2907502017 hasConceptScore W2907502017C196921405 @default.
- W2907502017 hasConceptScore W2907502017C2524010 @default.
- W2907502017 hasConceptScore W2907502017C2780586970 @default.
- W2907502017 hasConceptScore W2907502017C33923547 @default.
- W2907502017 hasConceptScore W2907502017C41008148 @default.
- W2907502017 hasConceptScore W2907502017C46686674 @default.
- W2907502017 hasConceptScore W2907502017C77805123 @default.
- W2907502017 hasConceptScore W2907502017C8880873 @default.
- W2907502017 hasIssue "12" @default.
- W2907502017 hasLocation W29075020171 @default.
- W2907502017 hasOpenAccess W2907502017 @default.
- W2907502017 hasPrimaryLocation W29075020171 @default.
- W2907502017 hasRelatedWork W1996541855 @default.
- W2907502017 hasRelatedWork W2006266337 @default.
- W2907502017 hasRelatedWork W2156053740 @default.
- W2907502017 hasRelatedWork W2907502017 @default.
- W2907502017 hasRelatedWork W2937631562 @default.
- W2907502017 hasRelatedWork W3194539120 @default.
- W2907502017 hasRelatedWork W3195168932 @default.
- W2907502017 hasRelatedWork W4225307033 @default.
- W2907502017 hasRelatedWork W4283697347 @default.
- W2907502017 hasRelatedWork W4361795583 @default.
- W2907502017 hasVolume "9" @default.
- W2907502017 isParatext "false" @default.
- W2907502017 isRetracted "false" @default.
- W2907502017 magId "2907502017" @default.
- W2907502017 workType "article" @default.