Matches in SemOpenAlex for { <https://semopenalex.org/work/W2756254222> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W2756254222 endingPage "62" @default.
- W2756254222 startingPage "51" @default.
- W2756254222 abstract "Feature selection is essential to improve the classification effectiveness. This paper presents a new adaptive algorithm called FS-PeSOA (feature selection penguins search optimization algorithm) which is a meta-heuristic feature selection method based on “Penguins Search Optimization Algorithm” (PeSOA), it will be combined with different classifiers to find the best subset features, which achieve the highest accuracy in classification. In order to explore the feature subset candidates, the bio-inspired approach PeSOA generates during the process a trial feature subset and estimates its fitness value by using three classifiers for each case: Naive Bayes (NB), Nearest Neighbors (KNN) and Support Vector Machines (SVMs). Our proposed approach has been experimented on six well known benchmark datasets (Wisconsin Breast Cancer, Pima Diabetes, Mammographic Mass, Dermatology, Colon Tumor and Prostate Cancer data sets). Experimental results prove that the classification accuracy of FS-PeSOA is the highest and very powerful for different datasets." @default.
- W2756254222 created "2017-09-25" @default.
- W2756254222 creator A5026347392 @default.
- W2756254222 creator A5036568214 @default.
- W2756254222 date "2017-10-01" @default.
- W2756254222 modified "2023-09-26" @default.
- W2756254222 title "Using Penguins Search Optimization Algorithm for Best Features Selection for Biomedical Data Classification" @default.
- W2756254222 cites W1513941701 @default.
- W2756254222 cites W1608373179 @default.
- W2756254222 cites W1906176972 @default.
- W2756254222 cites W1976610059 @default.
- W2756254222 cites W2031659228 @default.
- W2756254222 cites W2036271857 @default.
- W2756254222 cites W2038894244 @default.
- W2756254222 cites W2047099264 @default.
- W2756254222 cites W2057891902 @default.
- W2756254222 cites W2091087636 @default.
- W2756254222 cites W2099884867 @default.
- W2756254222 cites W2129659619 @default.
- W2756254222 cites W2139212933 @default.
- W2756254222 cites W2142272989 @default.
- W2756254222 cites W2149684865 @default.
- W2756254222 cites W2215643646 @default.
- W2756254222 cites W2329094193 @default.
- W2756254222 cites W2331881048 @default.
- W2756254222 cites W2497088069 @default.
- W2756254222 doi "https://doi.org/10.4018/ijoci.2017100103" @default.
- W2756254222 hasPublicationYear "2017" @default.
- W2756254222 type Work @default.
- W2756254222 sameAs 2756254222 @default.
- W2756254222 citedByCount "2" @default.
- W2756254222 countsByYear W27562542222018 @default.
- W2756254222 countsByYear W27562542222021 @default.
- W2756254222 crossrefType "journal-article" @default.
- W2756254222 hasAuthorship W2756254222A5026347392 @default.
- W2756254222 hasAuthorship W2756254222A5036568214 @default.
- W2756254222 hasConcept C11413529 @default.
- W2756254222 hasConcept C119857082 @default.
- W2756254222 hasConcept C12267149 @default.
- W2756254222 hasConcept C124101348 @default.
- W2756254222 hasConcept C13280743 @default.
- W2756254222 hasConcept C138885662 @default.
- W2756254222 hasConcept C148483581 @default.
- W2756254222 hasConcept C153180895 @default.
- W2756254222 hasConcept C154945302 @default.
- W2756254222 hasConcept C185798385 @default.
- W2756254222 hasConcept C205649164 @default.
- W2756254222 hasConcept C2776401178 @default.
- W2756254222 hasConcept C41008148 @default.
- W2756254222 hasConcept C41895202 @default.
- W2756254222 hasConcept C52001869 @default.
- W2756254222 hasConcept C81917197 @default.
- W2756254222 hasConceptScore W2756254222C11413529 @default.
- W2756254222 hasConceptScore W2756254222C119857082 @default.
- W2756254222 hasConceptScore W2756254222C12267149 @default.
- W2756254222 hasConceptScore W2756254222C124101348 @default.
- W2756254222 hasConceptScore W2756254222C13280743 @default.
- W2756254222 hasConceptScore W2756254222C138885662 @default.
- W2756254222 hasConceptScore W2756254222C148483581 @default.
- W2756254222 hasConceptScore W2756254222C153180895 @default.
- W2756254222 hasConceptScore W2756254222C154945302 @default.
- W2756254222 hasConceptScore W2756254222C185798385 @default.
- W2756254222 hasConceptScore W2756254222C205649164 @default.
- W2756254222 hasConceptScore W2756254222C2776401178 @default.
- W2756254222 hasConceptScore W2756254222C41008148 @default.
- W2756254222 hasConceptScore W2756254222C41895202 @default.
- W2756254222 hasConceptScore W2756254222C52001869 @default.
- W2756254222 hasConceptScore W2756254222C81917197 @default.
- W2756254222 hasIssue "4" @default.
- W2756254222 hasLocation W27562542221 @default.
- W2756254222 hasOpenAccess W2756254222 @default.
- W2756254222 hasPrimaryLocation W27562542221 @default.
- W2756254222 hasRelatedWork W2539163683 @default.
- W2756254222 hasRelatedWork W2595988085 @default.
- W2756254222 hasRelatedWork W2979979539 @default.
- W2756254222 hasRelatedWork W2985924212 @default.
- W2756254222 hasRelatedWork W3105251098 @default.
- W2756254222 hasRelatedWork W3210877509 @default.
- W2756254222 hasRelatedWork W4205958290 @default.
- W2756254222 hasRelatedWork W4212852473 @default.
- W2756254222 hasRelatedWork W4213444042 @default.
- W2756254222 hasRelatedWork W2345184372 @default.
- W2756254222 hasVolume "7" @default.
- W2756254222 isParatext "false" @default.
- W2756254222 isRetracted "false" @default.
- W2756254222 magId "2756254222" @default.
- W2756254222 workType "article" @default.