Matches in SemOpenAlex for { <https://semopenalex.org/work/W1569704244> ?p ?o ?g. }
Showing items 1 to 84 of
84
with 100 items per page.
- W1569704244 endingPage "144" @default.
- W1569704244 startingPage "127" @default.
- W1569704244 abstract "AbstractBiomedical datasets pose a unique challenge for machine learning and data mining techniques to extract accurate, comprehensible and hidden knowledge from them. In this paper, we investigate the role of a biomedical dataset on the classification accuracy of an algorithm. To this end, we quantify the complexity of a biomedical dataset in terms of its missing values, imbalance ratio, noise and information gain. We have performed our experiments using six well-known evolutionary rule learning algorithms – XCS, UCS, GAssist, cAnt-Miner, SLAVE and Ishibuchi – on 31 publicly available biomedical datasets. The results of our experiments and statistical analysis show that GAssist gives better classification results on majority of biomedical datasets among the compared schemes but cannot be categorized as the best classifier. Moreover, our analysis reveals that the nature of a biomedical dataset – not the selection of evolutionary algorithm – plays a major role in determining the classification accuracy of a dataset. We further show that noise is a dominating factor in determining the complexity of a dataset and it is inversely proportional to the classification accuracy of all evaluated algorithms. Towards the end, we provide researchers with a meta-classification model that can be used to determine the classification potential of a dataset on the basis of its complexity measures.KeywordsClassificationEvolutionary Rule Learning AlgorithmsBiomedical DatasetsPerformance Measures" @default.
- W1569704244 created "2016-06-24" @default.
- W1569704244 creator A5074963675 @default.
- W1569704244 creator A5088063475 @default.
- W1569704244 date "2010-01-01" @default.
- W1569704244 modified "2023-10-13" @default.
- W1569704244 title "Classification Potential vs. Classification Accuracy: A Comprehensive Study of Evolutionary Algorithms with Biomedical Datasets" @default.
- W1569704244 cites W1487816274 @default.
- W1569704244 cites W1511618494 @default.
- W1569704244 cites W1567602083 @default.
- W1569704244 cites W1579274558 @default.
- W1569704244 cites W1581513707 @default.
- W1569704244 cites W1769852110 @default.
- W1569704244 cites W1837359969 @default.
- W1569704244 cites W1972071974 @default.
- W1569704244 cites W2000950277 @default.
- W1569704244 cites W2011376672 @default.
- W1569704244 cites W2016944307 @default.
- W1569704244 cites W2022849555 @default.
- W1569704244 cites W2034841618 @default.
- W1569704244 cites W2037990811 @default.
- W1569704244 cites W2061554433 @default.
- W1569704244 cites W2089939419 @default.
- W1569704244 cites W2098888151 @default.
- W1569704244 cites W2108821163 @default.
- W1569704244 cites W2125965138 @default.
- W1569704244 cites W2127021155 @default.
- W1569704244 cites W2134367663 @default.
- W1569704244 cites W2135423934 @default.
- W1569704244 cites W2145063378 @default.
- W1569704244 cites W2146713522 @default.
- W1569704244 cites W3100570787 @default.
- W1569704244 doi "https://doi.org/10.1007/978-3-642-17508-4_9" @default.
- W1569704244 hasPublicationYear "2010" @default.
- W1569704244 type Work @default.
- W1569704244 sameAs 1569704244 @default.
- W1569704244 citedByCount "14" @default.
- W1569704244 countsByYear W15697042442013 @default.
- W1569704244 countsByYear W15697042442018 @default.
- W1569704244 countsByYear W15697042442019 @default.
- W1569704244 countsByYear W15697042442020 @default.
- W1569704244 countsByYear W15697042442021 @default.
- W1569704244 countsByYear W15697042442022 @default.
- W1569704244 countsByYear W15697042442023 @default.
- W1569704244 crossrefType "book-chapter" @default.
- W1569704244 hasAuthorship W1569704244A5074963675 @default.
- W1569704244 hasAuthorship W1569704244A5088063475 @default.
- W1569704244 hasBestOaLocation W15697042442 @default.
- W1569704244 hasConcept C110083411 @default.
- W1569704244 hasConcept C11413529 @default.
- W1569704244 hasConcept C119857082 @default.
- W1569704244 hasConcept C124101348 @default.
- W1569704244 hasConcept C154945302 @default.
- W1569704244 hasConcept C159149176 @default.
- W1569704244 hasConcept C41008148 @default.
- W1569704244 hasConcept C95623464 @default.
- W1569704244 hasConceptScore W1569704244C110083411 @default.
- W1569704244 hasConceptScore W1569704244C11413529 @default.
- W1569704244 hasConceptScore W1569704244C119857082 @default.
- W1569704244 hasConceptScore W1569704244C124101348 @default.
- W1569704244 hasConceptScore W1569704244C154945302 @default.
- W1569704244 hasConceptScore W1569704244C159149176 @default.
- W1569704244 hasConceptScore W1569704244C41008148 @default.
- W1569704244 hasConceptScore W1569704244C95623464 @default.
- W1569704244 hasLocation W15697042441 @default.
- W1569704244 hasLocation W15697042442 @default.
- W1569704244 hasOpenAccess W1569704244 @default.
- W1569704244 hasPrimaryLocation W15697042441 @default.
- W1569704244 hasRelatedWork W2512018286 @default.
- W1569704244 hasRelatedWork W2556319748 @default.
- W1569704244 hasRelatedWork W2623427976 @default.
- W1569704244 hasRelatedWork W2961085424 @default.
- W1569704244 hasRelatedWork W3158264953 @default.
- W1569704244 hasRelatedWork W3200179079 @default.
- W1569704244 hasRelatedWork W4249229055 @default.
- W1569704244 hasRelatedWork W4282839226 @default.
- W1569704244 hasRelatedWork W4306321456 @default.
- W1569704244 hasRelatedWork W4310989423 @default.
- W1569704244 isParatext "false" @default.
- W1569704244 isRetracted "false" @default.
- W1569704244 magId "1569704244" @default.
- W1569704244 workType "book-chapter" @default.