Fusion of residual network and t-SNE-CS for 2D visualization of open set recognition
Open set recognition (OSR) is a technique employed to ascertain whether unknown data belongs to a class in a database when the training class is incomplete. In addressing the OSR challenge associated with ADS-B leading pulse signals, this paper proposes a two-dimensional visualization of open set re...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Intelligent Systems with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305324001388 |
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| author | Tong Xu |
| author_facet | Tong Xu |
| author_sort | Tong Xu |
| collection | DOAJ |
| description | Open set recognition (OSR) is a technique employed to ascertain whether unknown data belongs to a class in a database when the training class is incomplete. In addressing the OSR challenge associated with ADS-B leading pulse signals, this paper proposes a two-dimensional visualization of open set recognition (VOSR) approach that encompasses the stages of feature extraction, feature selection, and feature learning levels. At the feature extraction level, the I/Q features and phase features of the signal are selected; at the feature selection level, feature similarity analysis and mean decrease impurity-based random forest are employed; at the feature learning level, the framework of fusion residual network and the t-distributed stochastic neighbor embedding and circular surfaces (t-SNE-CS) strategy is constructed, and experiments are carried out on the close set data containing 20 classes of 10,229 samples and open set data containing 10 classes of 1,688 samples. Results show that the accuracy of the optimal combination of the residual network and the constructed features is 94.63% for the test set for the close set classification task. For the VOSR task, the accuracy of the test set is 93.69%, the open set recognition accuracy is 53.97% and Macro-F1 scores is 91.8%. |
| format | Article |
| id | doaj-art-b7878ba406d841599d8118b5917bd97c |
| institution | Kabale University |
| issn | 2667-3053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj-art-b7878ba406d841599d8118b5917bd97c2024-12-13T11:07:29ZengElsevierIntelligent Systems with Applications2667-30532024-12-0124200464Fusion of residual network and t-SNE-CS for 2D visualization of open set recognitionTong Xu0School of Information Technology, Jiangsu Open University, Nanjing 210000, ChinaOpen set recognition (OSR) is a technique employed to ascertain whether unknown data belongs to a class in a database when the training class is incomplete. In addressing the OSR challenge associated with ADS-B leading pulse signals, this paper proposes a two-dimensional visualization of open set recognition (VOSR) approach that encompasses the stages of feature extraction, feature selection, and feature learning levels. At the feature extraction level, the I/Q features and phase features of the signal are selected; at the feature selection level, feature similarity analysis and mean decrease impurity-based random forest are employed; at the feature learning level, the framework of fusion residual network and the t-distributed stochastic neighbor embedding and circular surfaces (t-SNE-CS) strategy is constructed, and experiments are carried out on the close set data containing 20 classes of 10,229 samples and open set data containing 10 classes of 1,688 samples. Results show that the accuracy of the optimal combination of the residual network and the constructed features is 94.63% for the test set for the close set classification task. For the VOSR task, the accuracy of the test set is 93.69%, the open set recognition accuracy is 53.97% and Macro-F1 scores is 91.8%.http://www.sciencedirect.com/science/article/pii/S2667305324001388ADS-B preamble pulse signals2D visualization of open set recognitionCircular surfacest-distributed stochastic neighbor embedding |
| spellingShingle | Tong Xu Fusion of residual network and t-SNE-CS for 2D visualization of open set recognition Intelligent Systems with Applications ADS-B preamble pulse signals 2D visualization of open set recognition Circular surfaces t-distributed stochastic neighbor embedding |
| title | Fusion of residual network and t-SNE-CS for 2D visualization of open set recognition |
| title_full | Fusion of residual network and t-SNE-CS for 2D visualization of open set recognition |
| title_fullStr | Fusion of residual network and t-SNE-CS for 2D visualization of open set recognition |
| title_full_unstemmed | Fusion of residual network and t-SNE-CS for 2D visualization of open set recognition |
| title_short | Fusion of residual network and t-SNE-CS for 2D visualization of open set recognition |
| title_sort | fusion of residual network and t sne cs for 2d visualization of open set recognition |
| topic | ADS-B preamble pulse signals 2D visualization of open set recognition Circular surfaces t-distributed stochastic neighbor embedding |
| url | http://www.sciencedirect.com/science/article/pii/S2667305324001388 |
| work_keys_str_mv | AT tongxu fusionofresidualnetworkandtsnecsfor2dvisualizationofopensetrecognition |