Matches in SemOpenAlex for { <https://semopenalex.org/work/W4312561161> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W4312561161 abstract "Feature Extraction (EF) is considered the effective process among all the data processing steps of the classification system. In real-life applications, the reliability of a classifier is highly affected by high-dimensional irrelevant and redundant information. Hence extraction of appropriate data plays an imperative role to reduce the dimensionality and increase the performance of the classification system. Herein paper, a hybrid Principal Independent Component Analysis (PICA) technique is presented by the combination of the two most popular Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) feature extraction techniques. The authors execute the proposed PICA technique with the SGD classifier of machine learning (ML) and analyze the performance by comparing the results with existing PCA, LDA, SVD, and ICA feature extraction techniques. Furthermore, to evaluate the PICA's performance, results are compared without applying any feature extraction techniques or with existing ICA, PCA, LDA, and SVD methods. The effectiveness of the presented work is better than existing work found in the literature and is considered on an improved scale of accomplished 3.94% accuracy, 1.35% Sensitivity, 7.70% Specificity, and 5.27% precision. Moreover, decrease the 42.60% RMSE and 15% dimensionality." @default.
- W4312561161 created "2023-01-05" @default.
- W4312561161 creator A5014199830 @default.
- W4312561161 creator A5016311020 @default.
- W4312561161 creator A5037306332 @default.
- W4312561161 date "2022-10-07" @default.
- W4312561161 modified "2023-10-16" @default.
- W4312561161 title "Pica-A Hybrid Feature Extraction Technique Based on Principal Component Analysis and Independent Component Analysis" @default.
- W4312561161 cites W2077871208 @default.
- W4312561161 cites W2087016914 @default.
- W4312561161 cites W2125975118 @default.
- W4312561161 cites W2332145827 @default.
- W4312561161 cites W2601157893 @default.
- W4312561161 cites W2784726185 @default.
- W4312561161 cites W2790563929 @default.
- W4312561161 cites W2795055772 @default.
- W4312561161 cites W2913228117 @default.
- W4312561161 cites W2914886213 @default.
- W4312561161 cites W2956516036 @default.
- W4312561161 cites W2988647409 @default.
- W4312561161 cites W3000409346 @default.
- W4312561161 cites W3018189018 @default.
- W4312561161 cites W3097674592 @default.
- W4312561161 cites W3134668672 @default.
- W4312561161 cites W3173093567 @default.
- W4312561161 cites W3206166550 @default.
- W4312561161 cites W4212821816 @default.
- W4312561161 cites W4242903593 @default.
- W4312561161 doi "https://doi.org/10.1109/gcat55367.2022.9971838" @default.
- W4312561161 hasPublicationYear "2022" @default.
- W4312561161 type Work @default.
- W4312561161 citedByCount "0" @default.
- W4312561161 crossrefType "proceedings-article" @default.
- W4312561161 hasAuthorship W4312561161A5014199830 @default.
- W4312561161 hasAuthorship W4312561161A5016311020 @default.
- W4312561161 hasAuthorship W4312561161A5037306332 @default.
- W4312561161 hasConcept C111030470 @default.
- W4312561161 hasConcept C124101348 @default.
- W4312561161 hasConcept C153180895 @default.
- W4312561161 hasConcept C154945302 @default.
- W4312561161 hasConcept C22789450 @default.
- W4312561161 hasConcept C27438332 @default.
- W4312561161 hasConcept C41008148 @default.
- W4312561161 hasConcept C51432778 @default.
- W4312561161 hasConcept C52622490 @default.
- W4312561161 hasConcept C70518039 @default.
- W4312561161 hasConcept C95623464 @default.
- W4312561161 hasConceptScore W4312561161C111030470 @default.
- W4312561161 hasConceptScore W4312561161C124101348 @default.
- W4312561161 hasConceptScore W4312561161C153180895 @default.
- W4312561161 hasConceptScore W4312561161C154945302 @default.
- W4312561161 hasConceptScore W4312561161C22789450 @default.
- W4312561161 hasConceptScore W4312561161C27438332 @default.
- W4312561161 hasConceptScore W4312561161C41008148 @default.
- W4312561161 hasConceptScore W4312561161C51432778 @default.
- W4312561161 hasConceptScore W4312561161C52622490 @default.
- W4312561161 hasConceptScore W4312561161C70518039 @default.
- W4312561161 hasConceptScore W4312561161C95623464 @default.
- W4312561161 hasLocation W43125611611 @default.
- W4312561161 hasOpenAccess W4312561161 @default.
- W4312561161 hasPrimaryLocation W43125611611 @default.
- W4312561161 hasRelatedWork W1677378707 @default.
- W4312561161 hasRelatedWork W2108104958 @default.
- W4312561161 hasRelatedWork W2169954946 @default.
- W4312561161 hasRelatedWork W2373052636 @default.
- W4312561161 hasRelatedWork W2384185843 @default.
- W4312561161 hasRelatedWork W3018841469 @default.
- W4312561161 hasRelatedWork W3045294822 @default.
- W4312561161 hasRelatedWork W4312561161 @default.
- W4312561161 hasRelatedWork W4315926475 @default.
- W4312561161 hasRelatedWork W2185519626 @default.
- W4312561161 isParatext "false" @default.
- W4312561161 isRetracted "false" @default.
- W4312561161 workType "article" @default.