A hybrid quantum annealing method for generating ensemble classifiers
Quantum annealing has been widely used to optimize machine learning such as ensemble classifiers. This ensemble classifier enhances classification performance through the combination of multiple accurate and diverse base classifiers. It benefits from the “perturb and combine” strategy which involves...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823003853 |
| _version_ | 1797389535659687936 |
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| author | Lenny Putri Yulianti Agung Trisetyarso Judhi Santoso Kridanto Surendro |
| author_facet | Lenny Putri Yulianti Agung Trisetyarso Judhi Santoso Kridanto Surendro |
| author_sort | Lenny Putri Yulianti |
| collection | DOAJ |
| description | Quantum annealing has been widely used to optimize machine learning such as ensemble classifiers. This ensemble classifier enhances classification performance through the combination of multiple accurate and diverse base classifiers. It benefits from the “perturb and combine” strategy which involves using the random subspace as the prevalent approach to perturb input data. There are several methods to generate a random subspace which include bagging, boosting, clustering, and, more recently, clustering balancing. The main contribution of this research was to improve the accuracy of the ensemble classifier using the hybrid quantum annealing method. The process involved making larger, stronger, and more balanced clusters through 1) the modification of the range of values from K in incremental clustering, 2) the adjustment of the clusters to be stronger and more balanced, and 3) the optimization of pure-class clusters using quantum annealing. The proposed method was tested and evaluated on 10 benchmark datasets from the UCI repository and the results were compared with current approaches. The results showed that the proposed method has better accuracy than the others due to the larger, stronger, and more balanced clusters generated as well as the better trade-off between accuracy and diversity. |
| first_indexed | 2024-03-08T22:57:24Z |
| format | Article |
| id | doaj.art-4f221d49d1524c648b7713c07487e5a2 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-03-08T22:57:24Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-4f221d49d1524c648b7713c07487e5a22023-12-16T06:06:06ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-12-013510101831A hybrid quantum annealing method for generating ensemble classifiersLenny Putri Yulianti0Agung Trisetyarso1Judhi Santoso2Kridanto Surendro3School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung 40132, Indonesia; Corresponding author.School of Computer Science, Department of Mathematics, Bina Nusantara University, Jakarta 11480, IndonesiaSchool of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung 40132, IndonesiaSchool of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung 40132, IndonesiaQuantum annealing has been widely used to optimize machine learning such as ensemble classifiers. This ensemble classifier enhances classification performance through the combination of multiple accurate and diverse base classifiers. It benefits from the “perturb and combine” strategy which involves using the random subspace as the prevalent approach to perturb input data. There are several methods to generate a random subspace which include bagging, boosting, clustering, and, more recently, clustering balancing. The main contribution of this research was to improve the accuracy of the ensemble classifier using the hybrid quantum annealing method. The process involved making larger, stronger, and more balanced clusters through 1) the modification of the range of values from K in incremental clustering, 2) the adjustment of the clusters to be stronger and more balanced, and 3) the optimization of pure-class clusters using quantum annealing. The proposed method was tested and evaluated on 10 benchmark datasets from the UCI repository and the results were compared with current approaches. The results showed that the proposed method has better accuracy than the others due to the larger, stronger, and more balanced clusters generated as well as the better trade-off between accuracy and diversity.http://www.sciencedirect.com/science/article/pii/S1319157823003853Hybrid quantum annealingEnsemble classifiersEnsemble generationClustering balancingAccuracy |
| spellingShingle | Lenny Putri Yulianti Agung Trisetyarso Judhi Santoso Kridanto Surendro A hybrid quantum annealing method for generating ensemble classifiers Journal of King Saud University: Computer and Information Sciences Hybrid quantum annealing Ensemble classifiers Ensemble generation Clustering balancing Accuracy |
| title | A hybrid quantum annealing method for generating ensemble classifiers |
| title_full | A hybrid quantum annealing method for generating ensemble classifiers |
| title_fullStr | A hybrid quantum annealing method for generating ensemble classifiers |
| title_full_unstemmed | A hybrid quantum annealing method for generating ensemble classifiers |
| title_short | A hybrid quantum annealing method for generating ensemble classifiers |
| title_sort | hybrid quantum annealing method for generating ensemble classifiers |
| topic | Hybrid quantum annealing Ensemble classifiers Ensemble generation Clustering balancing Accuracy |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823003853 |
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