Matches in SemOpenAlex for { <https://semopenalex.org/work/W3181106183> ?p ?o ?g. }
Showing items 1 to 65 of
65
with 100 items per page.
- W3181106183 endingPage "172" @default.
- W3181106183 startingPage "141" @default.
- W3181106183 abstract "It is challenging for humans to enable visual knowledge discovery in data with more than 2–3 dimensions with a naked eye. This chapter explores the efficiency of discovering predictive machine learning models interactively using new Elliptic Paired coordinates (EPC) visualizations. It is shown that EPC are capable to visualize multidimensional data and support visual machine learning with preservation of multidimensional information in 2-D. Relative to parallel and radial coordinates, EPC visualization requires only a half of the visual elements for each n–D point. An interactive software system EllipseVis, which is developed in this work, processes high-dimensional datasets, creates EPC visualizations, and produces predictive classification models by discovering dominance rules in EPC. By using interactive and automatic processes it discovers zones in EPC with a high dominance of a single class. The EPC methodology has been successful in discovering non-linear predictive models with high coverage and precision in the computational experiments. This can benefit multiple domains by producing visually appealing dominance rules. This chapter presents results of successful testing the EPC non-linear methodology in experiments using real and simulated data, EPC generalized to the Dynamic Elliptic Paired Coordinates (DEPC), incorporation of the weights of coordinates to optimize the visual discovery, introduction of an alternative EPC design and introduction of the concept of incompact machine learning methodology based on EPC/DEPC." @default.
- W3181106183 created "2021-07-19" @default.
- W3181106183 creator A5014052479 @default.
- W3181106183 creator A5089775223 @default.
- W3181106183 date "2022-01-01" @default.
- W3181106183 modified "2023-09-24" @default.
- W3181106183 title "Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinates" @default.
- W3181106183 cites W2512304460 @default.
- W3181106183 cites W2587299461 @default.
- W3181106183 cites W2790435797 @default.
- W3181106183 cites W2807125113 @default.
- W3181106183 cites W2883424428 @default.
- W3181106183 cites W3087851140 @default.
- W3181106183 cites W3136063196 @default.
- W3181106183 cites W4214760807 @default.
- W3181106183 cites W4235296278 @default.
- W3181106183 doi "https://doi.org/10.1007/978-3-030-93119-3_5" @default.
- W3181106183 hasPublicationYear "2022" @default.
- W3181106183 type Work @default.
- W3181106183 sameAs 3181106183 @default.
- W3181106183 citedByCount "0" @default.
- W3181106183 crossrefType "book-chapter" @default.
- W3181106183 hasAuthorship W3181106183A5014052479 @default.
- W3181106183 hasAuthorship W3181106183A5089775223 @default.
- W3181106183 hasBestOaLocation W31811061832 @default.
- W3181106183 hasConcept C119857082 @default.
- W3181106183 hasConcept C124101348 @default.
- W3181106183 hasConcept C154945302 @default.
- W3181106183 hasConcept C172367668 @default.
- W3181106183 hasConcept C194226119 @default.
- W3181106183 hasConcept C199360897 @default.
- W3181106183 hasConcept C2777904410 @default.
- W3181106183 hasConcept C36464697 @default.
- W3181106183 hasConcept C41008148 @default.
- W3181106183 hasConcept C60011546 @default.
- W3181106183 hasConceptScore W3181106183C119857082 @default.
- W3181106183 hasConceptScore W3181106183C124101348 @default.
- W3181106183 hasConceptScore W3181106183C154945302 @default.
- W3181106183 hasConceptScore W3181106183C172367668 @default.
- W3181106183 hasConceptScore W3181106183C194226119 @default.
- W3181106183 hasConceptScore W3181106183C199360897 @default.
- W3181106183 hasConceptScore W3181106183C2777904410 @default.
- W3181106183 hasConceptScore W3181106183C36464697 @default.
- W3181106183 hasConceptScore W3181106183C41008148 @default.
- W3181106183 hasConceptScore W3181106183C60011546 @default.
- W3181106183 hasLocation W31811061831 @default.
- W3181106183 hasLocation W31811061832 @default.
- W3181106183 hasOpenAccess W3181106183 @default.
- W3181106183 hasPrimaryLocation W31811061831 @default.
- W3181106183 hasRelatedWork W113374983 @default.
- W3181106183 hasRelatedWork W2071476217 @default.
- W3181106183 hasRelatedWork W2093133953 @default.
- W3181106183 hasRelatedWork W2148329479 @default.
- W3181106183 hasRelatedWork W2281016067 @default.
- W3181106183 hasRelatedWork W26643119 @default.
- W3181106183 hasRelatedWork W2905547976 @default.
- W3181106183 hasRelatedWork W2908919443 @default.
- W3181106183 hasRelatedWork W4320352461 @default.
- W3181106183 hasRelatedWork W85273705 @default.
- W3181106183 isParatext "false" @default.
- W3181106183 isRetracted "false" @default.
- W3181106183 magId "3181106183" @default.
- W3181106183 workType "book-chapter" @default.