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In order to simulate different types of label noise more accurately, we first assumed that the labels of the datasets used were all correct, and in addition constructed the noise set using two method: the density peak noise set and the random noise set. Experimental results demonstrate that the TF-SVDD can effectively detect noisy label data, surpassing traditional support vector data description algorithms and other methods in terms of outlier detection accuracy, with the average accuracy mostly exceeding 50\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\%$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mo>%<\/mml:mo>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    , and even reaching 80\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\%$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mo>%<\/mml:mo>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    . Furthermore, one novel measure called \u2018confidence\u2019 is employed to rectify noisy labels in the data. Following the correction of noisy labels, the accuracy of image classification experiences a significant improvement, with the average promotion ratio mostly exceeding 10\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\%$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mo>%<\/mml:mo>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    , and reaching 30\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\%$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mo>%<\/mml:mo>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    .\n                  <\/jats:p>","DOI":"10.1007\/s40747-024-01356-9","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:02:01Z","timestamp":1709510521000},"page":"4157-4174","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The fuzzy support vector data description based on tightness for noisy label detection"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5486-9938","authenticated-orcid":false,"given":"Xiaoying","family":"Wu","sequence":"first","affiliation":[]},{"given":"Sanyang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yiguang","family":"Bai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"key":"1356_CR1","doi-asserted-by":"crossref","unstructured":"Nigam N, Dutta T, Gupta HP (2020) Impact of noisy labels in learning techniques: a survey. 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