Enhancing Laser-Induced Breakdown Spectroscopy Quantification Through Minimum Redundancy and Maximum Relevance-Based Feature Selection
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| Udgivet i: | Remote Sensing vol. 17, no. 3 (2025), p. 416 |
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| Hovedforfatter: | |
| Andre forfattere: | , , , , , , |
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MDPI AG
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| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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|---|---|---|---|
| 001 | 3165893906 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17030416 |2 doi | |
| 035 | |a 3165893906 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Wang, Manping | |
| 245 | 1 | |a Enhancing Laser-Induced Breakdown Spectroscopy Quantification Through Minimum Redundancy and Maximum Relevance-Based Feature Selection | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Laser-induced breakdown spectroscopy (LIBS) is a rapid, non-contact analytical technique that is widely applied in various fields. However, the high dimensionality and information redundancy of LIBS spectral data present challenges for effective model development. This study aims to assess the effectiveness of the minimum redundancy and maximum relevance (mRMR) method for feature selection in LIBS spectral data and to explore its adaptability across different predictive modeling approaches. Using the ChemCam LIBS dataset, we constructed predictive models with four quantitative methods: random forest (RF), support vector regression (SVR), back propagation neural network (BPNN), and partial least squares regression (PLSR). We compared the performance of mRMR-based feature selection with that of full-spectrum data and three other feature selection methods: competitive adaptive re-weighted sampling (CARS), Regressional ReliefF (RReliefF), and neighborhood component analysis (NCA). Our results demonstrate that the mRMR method significantly reduces the number of selected features while improving model performance. This study validates the effectiveness of the mRMR algorithm for LIBS feature extraction and highlights the potential of feature selection techniques to enhance predictive accuracy. The findings provide a valuable strategy for feature selection in LIBS data analysis and offer significant implications for the practical application of LIBS in predicting elemental content in geological samples. | |
| 653 | |a Feature extraction | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Adaptive sampling | ||
| 653 | |a Regression analysis | ||
| 653 | |a Back propagation networks | ||
| 653 | |a Least squares method | ||
| 653 | |a Data processing | ||
| 653 | |a Feature selection | ||
| 653 | |a Data analysis | ||
| 653 | |a Prediction models | ||
| 653 | |a Spectroscopy | ||
| 653 | |a Adaptability | ||
| 653 | |a Redundancy | ||
| 653 | |a Neural networks | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Support vector machines | ||
| 653 | |a Effectiveness | ||
| 653 | |a Variables | ||
| 653 | |a Quantitative analysis | ||
| 653 | |a Algorithms | ||
| 653 | |a Laser induced breakdown spectroscopy | ||
| 653 | |a Methods | ||
| 653 | |a Mars | ||
| 700 | 1 | |a Lu, Yang | |
| 700 | 1 | |a Liu, Man | |
| 700 | 1 | |a Cui, Fuhui | |
| 700 | 1 | |a Gao, Rongke | |
| 700 | 1 | |a Wang, Feifei | |
| 700 | 1 | |a Chen, Xiaozhe | |
| 700 | 1 | |a Yu, Liandong | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 3 (2025), p. 416 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3165893906/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3165893906/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3165893906/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |