Broad Learning Model with a Dual Feature Extraction Strategy for Classification
The broad learning system (BLS) is a brief, flat neural network structure that has shown effectiveness in various classification tasks. However, original input data with high dimensionality often contain superfluous and correlated information affecting recognition performance. Moreover, the large nu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/19/4087 |
| _version_ | 1797575532117753856 |
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| author | Qi Zhang Zuobin Ying Jianhang Zhou Jingzhang Sun Bob Zhang |
| author_facet | Qi Zhang Zuobin Ying Jianhang Zhou Jingzhang Sun Bob Zhang |
| author_sort | Qi Zhang |
| collection | DOAJ |
| description | The broad learning system (BLS) is a brief, flat neural network structure that has shown effectiveness in various classification tasks. However, original input data with high dimensionality often contain superfluous and correlated information affecting recognition performance. Moreover, the large number of randomly mapped feature nodes and enhancement nodes may also cause a risk of redundant information that interferes with the conciseness and performance of the broad learning paradigm. To address the above-mentioned issues, we aim to introduce a broad learning model with a dual feature extraction strategy (BLM_DFE). In particular, kernel principal component analysis (KPCA) is applied to process the original input data before extracting effective low-dimensional features for the broad learning model. Afterwards, we perform KPCA again to simplify the feature nodes and enhancement nodes in the broad learning architecture to obtain more compact nodes for classification. As a result, the proposed model has a more straightforward structure with fewer nodes and retains superior recognition performance. Extensive experiments on diverse datasets and comparisons with various popular classification approaches are investigated and evaluated to support the effectiveness of the proposed model (e.g., achieving the best result of 77.28%, compared with 61.44% achieved with the standard BLS, on the GT database). |
| first_indexed | 2024-03-10T21:39:56Z |
| format | Article |
| id | doaj.art-8ab345d13f054f7bb4d8847adbfbda2e |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T21:39:56Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-8ab345d13f054f7bb4d8847adbfbda2e2023-11-19T14:43:10ZengMDPI AGMathematics2227-73902023-09-011119408710.3390/math11194087Broad Learning Model with a Dual Feature Extraction Strategy for ClassificationQi Zhang0Zuobin Ying1Jianhang Zhou2Jingzhang Sun3Bob Zhang4Faculty of Data Science, City University of Macau, Macau SAR, ChinaFaculty of Data Science, City University of Macau, Macau SAR, ChinaDepartment of Computer and Information Science, University of Macau, Macau SAR, ChinaSchool of Cyberspace Security, Hainan University, Haikou 570228, ChinaDepartment of Computer and Information Science, University of Macau, Macau SAR, ChinaThe broad learning system (BLS) is a brief, flat neural network structure that has shown effectiveness in various classification tasks. However, original input data with high dimensionality often contain superfluous and correlated information affecting recognition performance. Moreover, the large number of randomly mapped feature nodes and enhancement nodes may also cause a risk of redundant information that interferes with the conciseness and performance of the broad learning paradigm. To address the above-mentioned issues, we aim to introduce a broad learning model with a dual feature extraction strategy (BLM_DFE). In particular, kernel principal component analysis (KPCA) is applied to process the original input data before extracting effective low-dimensional features for the broad learning model. Afterwards, we perform KPCA again to simplify the feature nodes and enhancement nodes in the broad learning architecture to obtain more compact nodes for classification. As a result, the proposed model has a more straightforward structure with fewer nodes and retains superior recognition performance. Extensive experiments on diverse datasets and comparisons with various popular classification approaches are investigated and evaluated to support the effectiveness of the proposed model (e.g., achieving the best result of 77.28%, compared with 61.44% achieved with the standard BLS, on the GT database).https://www.mdpi.com/2227-7390/11/19/4087broad learningfeature extractionkernel principal component analysisneural network |
| spellingShingle | Qi Zhang Zuobin Ying Jianhang Zhou Jingzhang Sun Bob Zhang Broad Learning Model with a Dual Feature Extraction Strategy for Classification Mathematics broad learning feature extraction kernel principal component analysis neural network |
| title | Broad Learning Model with a Dual Feature Extraction Strategy for Classification |
| title_full | Broad Learning Model with a Dual Feature Extraction Strategy for Classification |
| title_fullStr | Broad Learning Model with a Dual Feature Extraction Strategy for Classification |
| title_full_unstemmed | Broad Learning Model with a Dual Feature Extraction Strategy for Classification |
| title_short | Broad Learning Model with a Dual Feature Extraction Strategy for Classification |
| title_sort | broad learning model with a dual feature extraction strategy for classification |
| topic | broad learning feature extraction kernel principal component analysis neural network |
| url | https://www.mdpi.com/2227-7390/11/19/4087 |
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