Academic Journal
Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets
| Τίτλος: | Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets |
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| Συγγραφείς: | Durr e Nayab, Rehan Ullah Khan, Ali Mustafa Qamar |
| Πηγή: | Applied Computational Intelligence and Soft Computing, Vol 2023 (2023) |
| Στοιχεία εκδότη: | Wiley, 2023. |
| Έτος έκδοσης: | 2023 |
| Θεματικοί όροι: | Artificial neural network, Artificial intelligence, Support vector machine, Handling Imbalanced Data in Classification Problems, Health Professions, 02 engineering and technology, Boosting (machine learning), Pattern recognition (psychology), Bayesian probability, Learning with Noisy Labels in Machine Learning, Boosting, Bayes' theorem, Health Information Management, Artificial Intelligence, Support Vector Machines, Meta-Learning, Health Sciences, Machine learning, Decision tree, 0202 electrical engineering, electronic engineering, information engineering, Multilayer perceptron, Perceptron, Machine Learning in Healthcare and Medicine, Naive Bayes classifier, AdaBoost, QA75.5-76.95, 15. Life on land, Computer science, Random subspace method, 3. Good health, Electronic computers. Computer science, Computer Science, Physical Sciences, Classifier (UML), Robust Learning, Random forest |
| Περιγραφή: | This paper investigates the performance enhancement of base classifiers within the AdaBoost framework applied to medical datasets. Adaptive boosting (AdaBoost), being an instance of boosting, combines other classifiers to enhance their performance. We conducted a comprehensive experiment to assess the efficacy of twelve base classifiers with the AdaBoost framework, namely, Bayes network, decision stump, ZeroR, decision tree, Naïve Bayes, J-48, voted perceptron, random forest, bagging, random tree, stacking, and AdaBoost itself. The experiments are carried out on five datasets from the medical domain based on various types of cancers, i.e., global cancer map (GCM), lymphoma-I, lymphoma-II, leukaemia, and embryonal tumours. The evaluation focuses on the accuracy, precision, and efficiency of the base classifiers in the AdaBoost framework. The results show that the performance of Naïve Bayes, Bayes network, and voted perceptron is highly improved compared to the rest of the base classifiers, attaining accuracies as high as 94.74%, 97.78%, and 97.78%, respectively. The results also show that in most cases, the base classifiers perform better with AdaBoost compared to their performance, i.e., for voted perceptron, the accuracy is improved up to 13.34%.For bagging, it is improved by up to 7%. This research aims to identify such base classifiers with optimal boosting capabilities within the AdaBoost framework for medical datasets. The significance of these results is that they provide insight into the performance of the base classifiers when used in the boosting framework to enhance the classification performance of classifiers in scenarios where individual classifiers do not perform up to the mark. |
| Τύπος εγγράφου: | Article Other literature type |
| Περιγραφή αρχείου: | text/xhtml |
| Γλώσσα: | English |
| ISSN: | 1687-9732 1687-9724 |
| DOI: | 10.1155/2023/5542049 |
| DOI: | 10.60692/2dp3v-6j332 |
| DOI: | 10.60692/fdcht-4jq68 |
| Σύνδεσμος πρόσβασης: | https://doaj.org/article/6c6ac4c6cb1f463a848e65d30d56be90 |
| Rights: | CC BY |
| Αριθμός Καταχώρησης: | edsair.doi.dedup.....a9aba3a66a9fcd088f4573b50f26f9e4 |
| Βάση Δεδομένων: | OpenAIRE |
| ISSN: | 16879732 16879724 |
|---|---|
| DOI: | 10.1155/2023/5542049 |