FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385612891> ?p ?o ?g. }

• W4385612891 endingPage "101966" @default.
• W4385612891 startingPage "101966" @default.
• W4385612891 abstract "Multivariate data analysis, as an important research topic in the field of machine learning, focuses on how to utilize the intrinsic connection between feature variables and target variables. However, in the face of complex multivariate prediction environments, existing single prediction models often fail to obtain ideal results. Meanwhile, existing ensemble prediction models are not always adapted to certain complex data. Moreover, the randomness in the clustering process cannot guarantee the clustering accuracy. Therefore, to improve the model’s prediction accuracy and ability to adapt to complex data and reduce the impact of randomness on clustering accuracy, this paper designs a multivariate prediction model utilizing three-way clustering (TWC) and ensemble learning, which is named the TWC-EL model. First, the initial division of the sample set is realized by k-means clustering algorithm, and further the sample set is divided again via the k-means clustering algorithm to solve the problem of clustering accuracy. Then, the results of clustering twice are combined according to the difference in the number of intersection points and the distance from the samples to the center point of each cluster, and the core and fringe regions of each cluster in the initial clustering results are obtained, forming a new TWC method. Next, based on the correlation between the regions, the obtained core and fringe regions are classified into low-correlation, medium-correlation and high-correlation regions, and an ensemble prediction model is designed by combining the advantages of the Elman neural network model, the Extreme Learning Machine (ELM) model and the back propagation neural network (BPNN) model. Finally, the experimental analysis results exhibit that the constructed TWC-EL model is efficient and feasible, and points out the excellent performance compared with the existing prediction models. The validity of the TWC method and the ensemble prediction model in the proposed TWC-EL model are verified by experiments, respectively." @default.
• W4385612891 created "2023-08-07" @default.
• W4385612891 creator A5045112228 @default.
• W4385612891 creator A5056723975 @default.
• W4385612891 creator A5069969191 @default.
• W4385612891 date "2023-12-01" @default.
• W4385612891 modified "2023-10-17" @default.
• W4385612891 title "TWC-EL: A multivariate prediction model by the fusion of three-way clustering and ensemble learning" @default.
• W4385612891 cites W2004445488 @default.
• W4385612891 cites W2070813883 @default.
• W4385612891 cites W2089923511 @default.
• W4385612891 cites W2140405352 @default.
• W4385612891 cites W2297889545 @default.
• W4385612891 cites W2775155156 @default.
• W4385612891 cites W2801314826 @default.
• W4385612891 cites W2889077617 @default.
• W4385612891 cites W2947588826 @default.
• W4385612891 cites W2948387392 @default.
• W4385612891 cites W2957056027 @default.
• W4385612891 cites W2979028505 @default.
• W4385612891 cites W2989005637 @default.
• W4385612891 cites W2992258031 @default.
• W4385612891 cites W3014018326 @default.
• W4385612891 cites W3025327496 @default.
• W4385612891 cites W3034392970 @default.
• W4385612891 cites W3041767007 @default.
• W4385612891 cites W3041899150 @default.
• W4385612891 cites W3093243507 @default.
• W4385612891 cites W3120283405 @default.
• W4385612891 cites W3121235106 @default.
• W4385612891 cites W3136641731 @default.
• W4385612891 cites W3161620509 @default.
• W4385612891 cites W3164621419 @default.
• W4385612891 cites W3165437720 @default.
• W4385612891 cites W3196296792 @default.
• W4385612891 cites W3198541611 @default.
• W4385612891 cites W3201856258 @default.
• W4385612891 cites W3201991780 @default.
• W4385612891 cites W3206196543 @default.
• W4385612891 cites W4200021389 @default.
• W4385612891 cites W4206189171 @default.
• W4385612891 cites W4223610633 @default.
• W4385612891 cites W4224008185 @default.
• W4385612891 cites W4225329060 @default.
• W4385612891 cites W4226380823 @default.
• W4385612891 cites W4283272924 @default.
• W4385612891 cites W4285097436 @default.
• W4385612891 cites W4291034555 @default.
• W4385612891 cites W4303628746 @default.
• W4385612891 cites W4307903597 @default.
• W4385612891 cites W4309865424 @default.
• W4385612891 cites W4313215560 @default.
• W4385612891 cites W4313828103 @default.
• W4385612891 cites W4378978063 @default.
• W4385612891 doi "https://doi.org/10.1016/j.inffus.2023.101966" @default.
• W4385612891 hasPublicationYear "2023" @default.
• W4385612891 type Work @default.
• W4385612891 citedByCount "0" @default.
• W4385612891 crossrefType "journal-article" @default.
• W4385612891 hasAuthorship W4385612891A5045112228 @default.
• W4385612891 hasAuthorship W4385612891A5056723975 @default.
• W4385612891 hasAuthorship W4385612891A5069969191 @default.
• W4385612891 hasConcept C105795698 @default.
• W4385612891 hasConcept C119857082 @default.
• W4385612891 hasConcept C124101348 @default.
• W4385612891 hasConcept C125112378 @default.
• W4385612891 hasConcept C127413603 @default.
• W4385612891 hasConcept C146978453 @default.
• W4385612891 hasConcept C153180895 @default.
• W4385612891 hasConcept C154945302 @default.
• W4385612891 hasConcept C161584116 @default.
• W4385612891 hasConcept C33923547 @default.
• W4385612891 hasConcept C41008148 @default.
• W4385612891 hasConcept C45942800 @default.
• W4385612891 hasConcept C64543145 @default.
• W4385612891 hasConcept C73555534 @default.
• W4385612891 hasConcept C94641424 @default.
• W4385612891 hasConceptScore W4385612891C105795698 @default.
• W4385612891 hasConceptScore W4385612891C119857082 @default.
• W4385612891 hasConceptScore W4385612891C124101348 @default.
• W4385612891 hasConceptScore W4385612891C125112378 @default.
• W4385612891 hasConceptScore W4385612891C127413603 @default.
• W4385612891 hasConceptScore W4385612891C146978453 @default.
• W4385612891 hasConceptScore W4385612891C153180895 @default.
• W4385612891 hasConceptScore W4385612891C154945302 @default.
• W4385612891 hasConceptScore W4385612891C161584116 @default.
• W4385612891 hasConceptScore W4385612891C33923547 @default.
• W4385612891 hasConceptScore W4385612891C41008148 @default.
• W4385612891 hasConceptScore W4385612891C45942800 @default.
• W4385612891 hasConceptScore W4385612891C64543145 @default.
• W4385612891 hasConceptScore W4385612891C73555534 @default.
• W4385612891 hasConceptScore W4385612891C94641424 @default.
• W4385612891 hasFunder F4320321001 @default.
• W4385612891 hasLocation W43856128911 @default.
• W4385612891 hasOpenAccess W4385612891 @default.
• W4385612891 hasPrimaryLocation W43856128911 @default.
• W4385612891 hasRelatedWork W1513698804 @default.
• W4385612891 hasRelatedWork W1982811510 @default.

• next