Matches in SemOpenAlex for { <https://semopenalex.org/work/W1532667542> ?p ?o ?g. }
Showing items 1 to 56 of
56
with 100 items per page.
- W1532667542 abstract "In this paper, we describe a rough sets approach to classification and attribute extraction of a lymphoma cancer dataset. We verify the classification accuracy of the results obtained from rough sets with a two artificial neural network based classifiers (ANNs). Our primary goal was to produce a classifier and a set of rules that could be used in a predictive manner. The dataset consisted of a number of relevant clinical variables obtained from patients that were suspected of having some form of blood based cancer (lymphoma or leukaemia). Of the 18 attributes that were collected for this patient cohort, seven were useful with respect to outcome prediction. In addition, this study was able to predict with a high degree of accuracy whether or not the disease would undergo metastases." @default.
- W1532667542 created "2016-06-24" @default.
- W1532667542 creator A5046000639 @default.
- W1532667542 creator A5056770224 @default.
- W1532667542 date "2007-01-01" @default.
- W1532667542 modified "2023-10-14" @default.
- W1532667542 title "An Analysis of a Lymphoma/Leukaemia Dataset Using Rough Sets and Neural Networks" @default.
- W1532667542 cites W107910782 @default.
- W1532667542 cites W1557533423 @default.
- W1532667542 cites W2154345794 @default.
- W1532667542 cites W4255833381 @default.
- W1532667542 doi "https://doi.org/10.1007/978-3-540-77368-9_23" @default.
- W1532667542 hasPublicationYear "2007" @default.
- W1532667542 type Work @default.
- W1532667542 sameAs 1532667542 @default.
- W1532667542 citedByCount "2" @default.
- W1532667542 countsByYear W15326675422014 @default.
- W1532667542 countsByYear W15326675422015 @default.
- W1532667542 crossrefType "book-chapter" @default.
- W1532667542 hasAuthorship W1532667542A5046000639 @default.
- W1532667542 hasAuthorship W1532667542A5056770224 @default.
- W1532667542 hasConcept C111012933 @default.
- W1532667542 hasConcept C119857082 @default.
- W1532667542 hasConcept C124101348 @default.
- W1532667542 hasConcept C153180895 @default.
- W1532667542 hasConcept C154945302 @default.
- W1532667542 hasConcept C41008148 @default.
- W1532667542 hasConcept C50644808 @default.
- W1532667542 hasConcept C52622490 @default.
- W1532667542 hasConcept C95623464 @default.
- W1532667542 hasConceptScore W1532667542C111012933 @default.
- W1532667542 hasConceptScore W1532667542C119857082 @default.
- W1532667542 hasConceptScore W1532667542C124101348 @default.
- W1532667542 hasConceptScore W1532667542C153180895 @default.
- W1532667542 hasConceptScore W1532667542C154945302 @default.
- W1532667542 hasConceptScore W1532667542C41008148 @default.
- W1532667542 hasConceptScore W1532667542C50644808 @default.
- W1532667542 hasConceptScore W1532667542C52622490 @default.
- W1532667542 hasConceptScore W1532667542C95623464 @default.
- W1532667542 hasLocation W15326675421 @default.
- W1532667542 hasOpenAccess W1532667542 @default.
- W1532667542 hasPrimaryLocation W15326675421 @default.
- W1532667542 hasRelatedWork W2086609641 @default.
- W1532667542 hasRelatedWork W2348634840 @default.
- W1532667542 hasRelatedWork W2357547549 @default.
- W1532667542 hasRelatedWork W2365964736 @default.
- W1532667542 hasRelatedWork W2367622902 @default.
- W1532667542 hasRelatedWork W2378238884 @default.
- W1532667542 hasRelatedWork W2384201452 @default.
- W1532667542 hasRelatedWork W2386387936 @default.
- W1532667542 hasRelatedWork W2388172999 @default.
- W1532667542 hasRelatedWork W3107474891 @default.
- W1532667542 isParatext "false" @default.
- W1532667542 isRetracted "false" @default.
- W1532667542 magId "1532667542" @default.
- W1532667542 workType "book-chapter" @default.