Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385186449> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W4385186449 endingPage "279" @default.
- W4385186449 startingPage "273" @default.
- W4385186449 abstract "A significant challenge in high-dimensional and big data analysis is related to the classification and prediction of the variables of interest. The massive genetic datasets are complex. Gene expression datasets are enriched with useful genes that are associated with specific diseases such as cancer. In this study, we used two gene expression datasets from the Gene Expression Omnibus and preprocessed them before classification. We used optimal kernel principal component analysis in which the optimal kernel function was chosen for dataset dimensionality reduction and extraction of the most important features. The gene sets with a high validity index were collected using a combined hieratical clustering and optimal kernel principal component analysis (KHC-RLR) algorithm. Logistic regression is one of the most common methods for classification, and it has been shown to be a useful classification approach for gene expression data analysis. In this study, we used multi-class logistic regression to classify the collected gene sets. We found that ordinary logistic regression caused a major overfitting problem; therefore, we used regularized multi-class logistic regression to classify the gene sets. The proposed KHC-RLR algorithm showed a high performance and satisfied accuracy measures." @default.
- W4385186449 created "2023-07-25" @default.
- W4385186449 creator A5011960777 @default.
- W4385186449 date "2023-01-01" @default.
- W4385186449 modified "2023-09-27" @default.
- W4385186449 title "Improved Regularized Multi-class Logistic Regression for Gene Classification with Optimal Kernel PCA and HC Algorithm" @default.
- W4385186449 cites W1983107534 @default.
- W4385186449 cites W2060406328 @default.
- W4385186449 cites W2080442929 @default.
- W4385186449 cites W2092101233 @default.
- W4385186449 cites W2096352448 @default.
- W4385186449 cites W2126576544 @default.
- W4385186449 cites W2135046866 @default.
- W4385186449 cites W2167536907 @default.
- W4385186449 cites W2182000055 @default.
- W4385186449 cites W2563958747 @default.
- W4385186449 cites W2567164805 @default.
- W4385186449 cites W2765976734 @default.
- W4385186449 cites W2782754314 @default.
- W4385186449 cites W3020972774 @default.
- W4385186449 cites W3041775844 @default.
- W4385186449 cites W3159333245 @default.
- W4385186449 cites W3194269477 @default.
- W4385186449 cites W4234698323 @default.
- W4385186449 cites W4306194623 @default.
- W4385186449 doi "https://doi.org/10.1007/978-3-031-31982-2_31" @default.
- W4385186449 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37486504" @default.
- W4385186449 hasPublicationYear "2023" @default.
- W4385186449 type Work @default.
- W4385186449 citedByCount "0" @default.
- W4385186449 crossrefType "book-chapter" @default.
- W4385186449 hasAuthorship W4385186449A5011960777 @default.
- W4385186449 hasConcept C105795698 @default.
- W4385186449 hasConcept C114614502 @default.
- W4385186449 hasConcept C119857082 @default.
- W4385186449 hasConcept C151956035 @default.
- W4385186449 hasConcept C153180895 @default.
- W4385186449 hasConcept C154945302 @default.
- W4385186449 hasConcept C22019652 @default.
- W4385186449 hasConcept C27438332 @default.
- W4385186449 hasConcept C33923547 @default.
- W4385186449 hasConcept C41008148 @default.
- W4385186449 hasConcept C50644808 @default.
- W4385186449 hasConcept C61722155 @default.
- W4385186449 hasConcept C70518039 @default.
- W4385186449 hasConcept C73555534 @default.
- W4385186449 hasConcept C74193536 @default.
- W4385186449 hasConcept C74887250 @default.
- W4385186449 hasConcept C83546350 @default.
- W4385186449 hasConceptScore W4385186449C105795698 @default.
- W4385186449 hasConceptScore W4385186449C114614502 @default.
- W4385186449 hasConceptScore W4385186449C119857082 @default.
- W4385186449 hasConceptScore W4385186449C151956035 @default.
- W4385186449 hasConceptScore W4385186449C153180895 @default.
- W4385186449 hasConceptScore W4385186449C154945302 @default.
- W4385186449 hasConceptScore W4385186449C22019652 @default.
- W4385186449 hasConceptScore W4385186449C27438332 @default.
- W4385186449 hasConceptScore W4385186449C33923547 @default.
- W4385186449 hasConceptScore W4385186449C41008148 @default.
- W4385186449 hasConceptScore W4385186449C50644808 @default.
- W4385186449 hasConceptScore W4385186449C61722155 @default.
- W4385186449 hasConceptScore W4385186449C70518039 @default.
- W4385186449 hasConceptScore W4385186449C73555534 @default.
- W4385186449 hasConceptScore W4385186449C74193536 @default.
- W4385186449 hasConceptScore W4385186449C74887250 @default.
- W4385186449 hasConceptScore W4385186449C83546350 @default.
- W4385186449 hasLocation W43851864491 @default.
- W4385186449 hasLocation W43851864492 @default.
- W4385186449 hasOpenAccess W4385186449 @default.
- W4385186449 hasPrimaryLocation W43851864491 @default.
- W4385186449 hasRelatedWork W1573501504 @default.
- W4385186449 hasRelatedWork W1998640076 @default.
- W4385186449 hasRelatedWork W2071626605 @default.
- W4385186449 hasRelatedWork W2136633751 @default.
- W4385186449 hasRelatedWork W2145759202 @default.
- W4385186449 hasRelatedWork W2146640661 @default.
- W4385186449 hasRelatedWork W2383760346 @default.
- W4385186449 hasRelatedWork W2989932438 @default.
- W4385186449 hasRelatedWork W4362564116 @default.
- W4385186449 hasRelatedWork W4375930476 @default.
- W4385186449 isParatext "false" @default.
- W4385186449 isRetracted "false" @default.
- W4385186449 workType "book-chapter" @default.