Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Durr e Nayab, Rehan Ullah Khan, A. M. Qamar
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引用次数: 0

Abstract

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.
针对医学数据集使用自适应提升框架增强基础分类器的性能
本文研究了将 AdaBoost 框架应用于医疗数据集的基础分类器的性能提升。自适应提升(AdaBoost)作为提升的一个实例,结合了其他分类器以提高其性能。我们进行了一项综合实验,以评估 AdaBoost 框架下十二种基础分类器的功效,它们分别是贝叶斯网络、决策桩、ZeroR、决策树、奈夫贝叶斯、J-48、投票感知器、随机森林、bagging、随机树、堆叠和 AdaBoost 本身。实验在医学领域的五个数据集上进行,这些数据集基于不同类型的癌症,即全球癌症图谱(GCM)、淋巴瘤-I、淋巴瘤-II、白血病和胚胎肿瘤。评估的重点是 AdaBoost 框架中基础分类器的准确度、精确度和效率。结果表明,与其他基础分类器相比,奈夫贝叶斯、贝叶斯网络和投票感知器的性能有了很大提高,准确率分别高达 94.74%、97.78% 和 97.78%。结果还显示,在大多数情况下,使用 AdaBoost 后,基础分类器的表现比它们的表现更好,例如,投票感知器的准确率提高了 13.34%,而装袋分类器的准确率提高了 7%。这项研究的目的是在 AdaBoost 框架内为医学数据集确定具有最佳提升能力的基础分类器。这些结果的意义在于,当基础分类器被用于提升框架时,它们能让人深入了解其性能,从而在单个分类器性能不达标的情况下提高分类器的分类性能。
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来源期刊
Applied Computational Intelligence and Soft Computing
Applied Computational Intelligence and Soft Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
6.10
自引率
3.40%
发文量
59
审稿时长
21 weeks
期刊介绍: Applied Computational Intelligence and Soft Computing will focus on the disciplines of computer science, engineering, and mathematics. The scope of the journal includes developing applications related to all aspects of natural and social sciences by employing the technologies of computational intelligence and soft computing. The new applications of using computational intelligence and soft computing are still in development. Although computational intelligence and soft computing are established fields, the new applications of using computational intelligence and soft computing can be regarded as an emerging field, which is the focus of this journal.
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