Department of Mathematics, University of Patras, Patras, Greece.
Department of Electrical & Computer Engineering, University of Patras, Patras, Greece.
Adv Exp Med Biol. 2020;1194:331-342. doi: 10.1007/978-3-030-32622-7_31.
Nowadays, cancer constitutes the second leading cause of death globally. The application of an efficient classification model is considered essential in modern diagnostic medicine in order to assist experts and physicians to make more accurate and early predictions and reduce the rate of mortality. Machine learning techniques are being broadly utilized for the development of intelligent computational systems, exploiting the recent advances in digital technologies and the significant storage capabilities of electronic media. Ensemble learning algorithms and semi-supervised algorithms have been independently developed to build efficient and robust classification models from different perspectives. The former attempts to achieve strong generalization by using multiple learners, while the latter attempts to achieve strong generalization by exploiting unlabeled data. In this work, we propose an improved semi-supervised self-labeled algorithm for cancer prediction, based on ensemble methodologies. Our preliminary numerical experiments illustrate the efficacy and efficiency of the proposed algorithm, proving that reliable and robust prediction models could be developed by the adaptation of ensemble techniques in the semi-supervised learning framework.
如今，癌症是全球第二大致死原因。在现代诊断医学中，应用有效的分类模型被认为是至关重要的，以便帮助专家和医生做出更准确和早期的预测，降低死亡率。机器学习技术正在被广泛应用于开发智能计算系统，利用数字技术的最新进展和电子媒体的大容量存储能力。集成学习算法和半监督算法已经从不同的角度被独立开发出来，以构建高效和强大的分类模型。前者试图通过使用多个学习者来实现强泛化能力，而后者则试图通过利用未标记的数据来实现强泛化能力。在这项工作中，我们提出了一种基于集成方法的改进的半监督自标记癌症预测算法。我们的初步数值实验说明了所提出算法的有效性和效率，证明了通过在半监督学习框架中采用集成技术，可以开发出可靠和强大的预测模型。
