Matches in SemOpenAlex for { <https://semopenalex.org/work/W2765992969> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W2765992969 abstract "Data stream classification is fast growing research area due to increasing number of practical applications in modern technology. SPAM filtering, weather forecast are just two well known examples. Nonetheless, high pace of incoming data makes classical algorithm inefficient as they usually use batch processing methods. What more, the characteristic of the data can change over time what makes classifiers obsolete. Their continuous updating can help, but cannot by applied in classical batch algorithms. On-line or chunk base training could be a solution. The last one is based on repeated extracting data chunks from data stream and using them for adaptation. In case of many difficult classification tasks ensembles of classifies work much better that systems based on single one classifier. Unfortunately the ensembles require additional training of their fusion model. In this paper we present the ensemble for data stream classification and compare two optimisation methods used for its training: Genetic Algorithm and Simulated Annealing. Results of experiments on several benchmark datasets shows that both methods are equally effective in term of accuracy and outperform several competing methods." @default.
- W2765992969 created "2017-11-10" @default.
- W2765992969 creator A5048318227 @default.
- W2765992969 date "2017-11-03" @default.
- W2765992969 modified "2023-09-23" @default.
- W2765992969 title "Application of Genetic Algorithm and Simulated Annealing to Ensemble Classifier Training on Data Streams" @default.
- W2765992969 cites W13338109 @default.
- W2765992969 cites W1512383952 @default.
- W2765992969 cites W1537430301 @default.
- W2765992969 cites W1585854823 @default.
- W2765992969 cites W1599232982 @default.
- W2765992969 cites W1990079212 @default.
- W2765992969 cites W2000454347 @default.
- W2765992969 cites W2009727399 @default.
- W2765992969 cites W2013514562 @default.
- W2765992969 cites W2070134328 @default.
- W2765992969 cites W2082168306 @default.
- W2765992969 cites W2099419573 @default.
- W2765992969 cites W2289463038 @default.
- W2765992969 cites W4241546951 @default.
- W2765992969 doi "https://doi.org/10.1007/978-3-319-69835-9_25" @default.
- W2765992969 hasPublicationYear "2017" @default.
- W2765992969 type Work @default.
- W2765992969 sameAs 2765992969 @default.
- W2765992969 citedByCount "0" @default.
- W2765992969 crossrefType "book-chapter" @default.
- W2765992969 hasAuthorship W2765992969A5048318227 @default.
- W2765992969 hasConcept C11413529 @default.
- W2765992969 hasConcept C119857082 @default.
- W2765992969 hasConcept C124101348 @default.
- W2765992969 hasConcept C126980161 @default.
- W2765992969 hasConcept C154945302 @default.
- W2765992969 hasConcept C2778484313 @default.
- W2765992969 hasConcept C41008148 @default.
- W2765992969 hasConcept C45942800 @default.
- W2765992969 hasConcept C51632099 @default.
- W2765992969 hasConcept C60777511 @default.
- W2765992969 hasConcept C76155785 @default.
- W2765992969 hasConcept C89198739 @default.
- W2765992969 hasConcept C95623464 @default.
- W2765992969 hasConceptScore W2765992969C11413529 @default.
- W2765992969 hasConceptScore W2765992969C119857082 @default.
- W2765992969 hasConceptScore W2765992969C124101348 @default.
- W2765992969 hasConceptScore W2765992969C126980161 @default.
- W2765992969 hasConceptScore W2765992969C154945302 @default.
- W2765992969 hasConceptScore W2765992969C2778484313 @default.
- W2765992969 hasConceptScore W2765992969C41008148 @default.
- W2765992969 hasConceptScore W2765992969C45942800 @default.
- W2765992969 hasConceptScore W2765992969C51632099 @default.
- W2765992969 hasConceptScore W2765992969C60777511 @default.
- W2765992969 hasConceptScore W2765992969C76155785 @default.
- W2765992969 hasConceptScore W2765992969C89198739 @default.
- W2765992969 hasConceptScore W2765992969C95623464 @default.
- W2765992969 hasLocation W27659929691 @default.
- W2765992969 hasOpenAccess W2765992969 @default.
- W2765992969 hasPrimaryLocation W27659929691 @default.
- W2765992969 hasRelatedWork W1567589860 @default.
- W2765992969 hasRelatedWork W1570012208 @default.
- W2765992969 hasRelatedWork W1782970676 @default.
- W2765992969 hasRelatedWork W1990079212 @default.
- W2765992969 hasRelatedWork W2086931437 @default.
- W2765992969 hasRelatedWork W2090464118 @default.
- W2765992969 hasRelatedWork W2155714768 @default.
- W2765992969 hasRelatedWork W2406139812 @default.
- W2765992969 hasRelatedWork W2584805731 @default.
- W2765992969 hasRelatedWork W2587314205 @default.
- W2765992969 hasRelatedWork W2742275837 @default.
- W2765992969 hasRelatedWork W2792149943 @default.
- W2765992969 hasRelatedWork W2811507669 @default.
- W2765992969 hasRelatedWork W2884885372 @default.
- W2765992969 hasRelatedWork W2904738814 @default.
- W2765992969 hasRelatedWork W2909288227 @default.
- W2765992969 hasRelatedWork W2965325977 @default.
- W2765992969 hasRelatedWork W2986218551 @default.
- W2765992969 hasRelatedWork W3207280834 @default.
- W2765992969 hasRelatedWork W53053957 @default.
- W2765992969 isParatext "false" @default.
- W2765992969 isRetracted "false" @default.
- W2765992969 magId "2765992969" @default.
- W2765992969 workType "book-chapter" @default.