BiCSA-PUL: binary crow search algorithm for enhancing positive and unlabeled learning
This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm repr...
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| Published in: | International journal of information technology (Singapore. Online) Vol. 17; no. 3; pp. 1729 - 1743 |
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| Language: | English |
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| ISSN: | 2511-2104, 2511-2112 |
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| Abstract | This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling. |
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| AbstractList | This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling. |
| Author | Ben Othmane, Mohamed Hamdi-Cherif, Aboubekeur Haouassi, Hichem Siam, Abderrahim Azizi, Nabil Ledmi, Makhlouf Hamouma, Moumen |
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| SubjectTerms | Accuracy Adaptation Algorithms Artificial Intelligence Bias Classification Computer Imaging Computer Science Datasets Distance learning Heuristic methods Image Processing and Computer Vision Machine Learning Medical diagnosis Methods Optimization techniques Original Research Pattern Recognition and Graphics Samples Search algorithms Social networks Software Engineering Support vector machines Vision |
| Title | BiCSA-PUL: binary crow search algorithm for enhancing positive and unlabeled learning |
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