Boosting With Prior for Accurate Classification
Adaptive Boosting (AdaBoost) based meta learning algorithms generate an accurate classifier ensemble using a learning algorithm with only moderate accuracy guarantees. These algorithms have been designed to work in typical supervised learning settings and hence use only labeled training data along w...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10139801/ |
| _version_ | 1797545231891038208 |
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| author | Mubasher Baig Tahir Ejaz Khawaja M. Fahad Syed Asif Mehmood Gilani Mian M. Awais Sana Saeed |
| author_facet | Mubasher Baig Tahir Ejaz Khawaja M. Fahad Syed Asif Mehmood Gilani Mian M. Awais Sana Saeed |
| author_sort | Mubasher Baig |
| collection | DOAJ |
| description | Adaptive Boosting (AdaBoost) based meta learning algorithms generate an accurate classifier ensemble using a learning algorithm with only moderate accuracy guarantees. These algorithms have been designed to work in typical supervised learning settings and hence use only labeled training data along with a base learning algorithm to form an ensemble. However, significant knowledge about the solution space might be available along with training data. The accuracy and convergence rate of AdaBoost might be improved using such knowledge. An effective way to incorporate such knowledge into boosting based ensemble learning algorithms is presented in this paper. Using several synthetic and real datasets, empirical evidence is reported to show the effectiveness of proposed method.Significant improvements have been obtained by applying the proposed method for detecting roads in aerial images. |
| first_indexed | 2024-03-10T14:12:34Z |
| format | Article |
| id | doaj.art-4cee57b83bdc421cb231b9cb37b23f18 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-10T14:12:34Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4cee57b83bdc421cb231b9cb37b23f182023-11-21T00:01:05ZengIEEEIEEE Access2169-35362023-01-011112812512813410.1109/ACCESS.2023.328168510139801Boosting With Prior for Accurate ClassificationMubasher Baig0https://orcid.org/0000-0003-2863-0736Tahir Ejaz1https://orcid.org/0009-0005-0620-288XKhawaja M. Fahad2Syed Asif Mehmood Gilani3https://orcid.org/0000-0001-9547-1689Mian M. Awais4Sana Saeed5Department of Computer Science, National University of Computer and Emerging Sciences (NUCES), Lahore, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences (NUCES), Lahore, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences (NUCES), Lahore, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences (NUCES), Lahore, PakistanDepartment of Computer Science, Lahore University of Management Sciences (LUMS), Lahore, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences (NUCES), Lahore, PakistanAdaptive Boosting (AdaBoost) based meta learning algorithms generate an accurate classifier ensemble using a learning algorithm with only moderate accuracy guarantees. These algorithms have been designed to work in typical supervised learning settings and hence use only labeled training data along with a base learning algorithm to form an ensemble. However, significant knowledge about the solution space might be available along with training data. The accuracy and convergence rate of AdaBoost might be improved using such knowledge. An effective way to incorporate such knowledge into boosting based ensemble learning algorithms is presented in this paper. Using several synthetic and real datasets, empirical evidence is reported to show the effectiveness of proposed method.Significant improvements have been obtained by applying the proposed method for detecting roads in aerial images.https://ieeexplore.ieee.org/document/10139801/AdaBoostensemble learningprior/domain knowledge |
| spellingShingle | Mubasher Baig Tahir Ejaz Khawaja M. Fahad Syed Asif Mehmood Gilani Mian M. Awais Sana Saeed Boosting With Prior for Accurate Classification IEEE Access AdaBoost ensemble learning prior/domain knowledge |
| title | Boosting With Prior for Accurate Classification |
| title_full | Boosting With Prior for Accurate Classification |
| title_fullStr | Boosting With Prior for Accurate Classification |
| title_full_unstemmed | Boosting With Prior for Accurate Classification |
| title_short | Boosting With Prior for Accurate Classification |
| title_sort | boosting with prior for accurate classification |
| topic | AdaBoost ensemble learning prior/domain knowledge |
| url | https://ieeexplore.ieee.org/document/10139801/ |
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