Matches in SemOpenAlex for { <https://semopenalex.org/work/W4328011284> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W4328011284 abstract "Existing localization model based on normal random forest achieved limited classification accuracy, the author proposes an improved random forest, and applies it in the loose particle localization method for sealed electronic equipment. Specifically, multiple classification regression trees (CARTs) that more than predetermined are trained, and three reserved sample subsets are used to calculate the average classification accuracy achieved by each CART. Based on this, CARTs are arranged in descending order. Meanwhile, the vectorial inner product method is used to calculate the inner product values among the CARTs. The overall classification accuracy achieved by the random forest and the grid search method are used to determine the optimal inner product threshold. The inner product values between each pair of CARTs are compared with the inner product threshold, and the one in pair of CARTs with an inner product value smaller than the inner product threshold that achieves a lower average classification accuracy is marked as erasable. On this basis, the average classification accuracies achieved by CARTs and the correlations between them are comprehensive considered, and those CARTs with higher correlation and lower classification accuracy are deleted until the number of remaining CARTs reaches the predetermined. They are used to construct a high-performance improved random forest, from which a better localization model is obtained. Experimental results show that, the precision, recall and F1-value obtained by the localization model based on improved random forest are substantially improved compared with the previous one. Meanwhile, it achieves a classification accuracy of 90.72%, which is 6.4 higher than the previous one of 84.25%. This fully demonstrates the superiority and stability of the proposed improved random forest, and also shows the feasibility and practicality of applying it to solve the loose particle localization problem. Theoretically, it can be applied to the acoustic emission source localization or fault source localization research in similar fields, and provide valuable reference for algorithm optimization in machine learning." @default.
- W4328011284 created "2023-03-22" @default.
- W4328011284 creator A5009483960 @default.
- W4328011284 creator A5014394467 @default.
- W4328011284 creator A5027895406 @default.
- W4328011284 creator A5065176807 @default.
- W4328011284 creator A5090569138 @default.
- W4328011284 date "2022-12-15" @default.
- W4328011284 modified "2023-09-26" @default.
- W4328011284 title "Loose Particle Localization Method for Sealed Electronic Equipment Based on Improved Random Forest" @default.
- W4328011284 cites W1702637483 @default.
- W4328011284 cites W1875028359 @default.
- W4328011284 cites W2043175314 @default.
- W4328011284 cites W2113242816 @default.
- W4328011284 cites W2214709255 @default.
- W4328011284 cites W2767419625 @default.
- W4328011284 cites W2790275230 @default.
- W4328011284 cites W2911964244 @default.
- W4328011284 cites W2962933532 @default.
- W4328011284 cites W2972922115 @default.
- W4328011284 cites W3083707628 @default.
- W4328011284 cites W3194667226 @default.
- W4328011284 cites W3204335069 @default.
- W4328011284 cites W4200172184 @default.
- W4328011284 cites W4212883601 @default.
- W4328011284 doi "https://doi.org/10.1109/srse56746.2022.10067854" @default.
- W4328011284 hasPublicationYear "2022" @default.
- W4328011284 type Work @default.
- W4328011284 citedByCount "0" @default.
- W4328011284 crossrefType "proceedings-article" @default.
- W4328011284 hasAuthorship W4328011284A5009483960 @default.
- W4328011284 hasAuthorship W4328011284A5014394467 @default.
- W4328011284 hasAuthorship W4328011284A5027895406 @default.
- W4328011284 hasAuthorship W4328011284A5065176807 @default.
- W4328011284 hasAuthorship W4328011284A5090569138 @default.
- W4328011284 hasConcept C105795698 @default.
- W4328011284 hasConcept C11413529 @default.
- W4328011284 hasConcept C124101348 @default.
- W4328011284 hasConcept C153180895 @default.
- W4328011284 hasConcept C154945302 @default.
- W4328011284 hasConcept C169258074 @default.
- W4328011284 hasConcept C22679943 @default.
- W4328011284 hasConcept C2524010 @default.
- W4328011284 hasConcept C33923547 @default.
- W4328011284 hasConcept C41008148 @default.
- W4328011284 hasConcept C90673727 @default.
- W4328011284 hasConceptScore W4328011284C105795698 @default.
- W4328011284 hasConceptScore W4328011284C11413529 @default.
- W4328011284 hasConceptScore W4328011284C124101348 @default.
- W4328011284 hasConceptScore W4328011284C153180895 @default.
- W4328011284 hasConceptScore W4328011284C154945302 @default.
- W4328011284 hasConceptScore W4328011284C169258074 @default.
- W4328011284 hasConceptScore W4328011284C22679943 @default.
- W4328011284 hasConceptScore W4328011284C2524010 @default.
- W4328011284 hasConceptScore W4328011284C33923547 @default.
- W4328011284 hasConceptScore W4328011284C41008148 @default.
- W4328011284 hasConceptScore W4328011284C90673727 @default.
- W4328011284 hasFunder F4320321001 @default.
- W4328011284 hasLocation W43280112841 @default.
- W4328011284 hasOpenAccess W4328011284 @default.
- W4328011284 hasPrimaryLocation W43280112841 @default.
- W4328011284 hasRelatedWork W2037342633 @default.
- W4328011284 hasRelatedWork W2275058042 @default.
- W4328011284 hasRelatedWork W2508925980 @default.
- W4328011284 hasRelatedWork W2893441059 @default.
- W4328011284 hasRelatedWork W2909139008 @default.
- W4328011284 hasRelatedWork W2964383635 @default.
- W4328011284 hasRelatedWork W2997958394 @default.
- W4328011284 hasRelatedWork W3044272884 @default.
- W4328011284 hasRelatedWork W3217110323 @default.
- W4328011284 hasRelatedWork W4242609709 @default.
- W4328011284 isParatext "false" @default.
- W4328011284 isRetracted "false" @default.
- W4328011284 workType "article" @default.