IHML: Incremental Heuristic Meta-Learner
The landscape of machine learning constantly demands innovative approaches to enhance algorithms’ performance across diverse tasks. Meta-learning, known as “learning to learn” is a promising way to overcome these diversity challenges by blending multiple algorithms. This study introduces the IHML: I...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2434309 |
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| author | Onur Karadeli Kıymet Kaya Şule Gündüz Öğüdücü |
| author_facet | Onur Karadeli Kıymet Kaya Şule Gündüz Öğüdücü |
| author_sort | Onur Karadeli |
| collection | DOAJ |
| description | The landscape of machine learning constantly demands innovative approaches to enhance algorithms’ performance across diverse tasks. Meta-learning, known as “learning to learn” is a promising way to overcome these diversity challenges by blending multiple algorithms. This study introduces the IHML: Incremental Heuristic Meta-Learner, a novel meta-learning algorithm for classification tasks. By leveraging a variety of base-learners with distinct learning dynamics, such as Gaussian, tree, and instance, IHML offers a comprehensive solution adaptable to different data characteristics. Moreover, the core contributions of IHML lie in its ability to tackle the optimal base-learner and feature sets determination mechanism with the help of Explainable Artificial Intelligence (XAI) and heuristic elbow methods. Existing work in this context utilizes XAI mostly in pre-processing the data or post-analysis of the results, however, IHML incorporates XAI into the learning process in an iterative manner and improves the prediction performance of the meta-learner. To observe the performance of the proposed IHML, we used five different datasets from astrophysics, physics, biology, e-commerce, and economics. The results show that the proposed model achieves more accuracy (in average % 10 and at most % 71 improvements) compared to the baseline machine learning models in the literature. |
| format | Article |
| id | doaj-art-0ad5d8985ea641c89334cee9a81cfbbd |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-0ad5d8985ea641c89334cee9a81cfbbd2024-12-16T16:13:01ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2434309IHML: Incremental Heuristic Meta-LearnerOnur Karadeli0Kıymet Kaya1Şule Gündüz Öğüdücü2Department of Computer Engineering, Istanbul Technical University, İstanbul, TurkeyDepartment of Computer Engineering, Istanbul Technical University, İstanbul, TurkeyITU AI Research and Application Center, İstanbul, TurkeyThe landscape of machine learning constantly demands innovative approaches to enhance algorithms’ performance across diverse tasks. Meta-learning, known as “learning to learn” is a promising way to overcome these diversity challenges by blending multiple algorithms. This study introduces the IHML: Incremental Heuristic Meta-Learner, a novel meta-learning algorithm for classification tasks. By leveraging a variety of base-learners with distinct learning dynamics, such as Gaussian, tree, and instance, IHML offers a comprehensive solution adaptable to different data characteristics. Moreover, the core contributions of IHML lie in its ability to tackle the optimal base-learner and feature sets determination mechanism with the help of Explainable Artificial Intelligence (XAI) and heuristic elbow methods. Existing work in this context utilizes XAI mostly in pre-processing the data or post-analysis of the results, however, IHML incorporates XAI into the learning process in an iterative manner and improves the prediction performance of the meta-learner. To observe the performance of the proposed IHML, we used five different datasets from astrophysics, physics, biology, e-commerce, and economics. The results show that the proposed model achieves more accuracy (in average % 10 and at most % 71 improvements) compared to the baseline machine learning models in the literature.https://www.tandfonline.com/doi/10.1080/08839514.2024.2434309 |
| spellingShingle | Onur Karadeli Kıymet Kaya Şule Gündüz Öğüdücü IHML: Incremental Heuristic Meta-Learner Applied Artificial Intelligence |
| title | IHML: Incremental Heuristic Meta-Learner |
| title_full | IHML: Incremental Heuristic Meta-Learner |
| title_fullStr | IHML: Incremental Heuristic Meta-Learner |
| title_full_unstemmed | IHML: Incremental Heuristic Meta-Learner |
| title_short | IHML: Incremental Heuristic Meta-Learner |
| title_sort | ihml incremental heuristic meta learner |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2434309 |
| work_keys_str_mv | AT onurkaradeli ihmlincrementalheuristicmetalearner AT kıymetkaya ihmlincrementalheuristicmetalearner AT sulegunduzoguducu ihmlincrementalheuristicmetalearner |