Ostvar Nasrin, Eftekhari Moghadam Amir Masoud
Faculty of Computer and Information Technology, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
Comput Intell Neurosci. 2020 Dec 14;2020:8826914. doi: 10.1155/2020/8826914. eCollection 2020.
In recent years, ensemble classification methods have been widely investigated in both industry and literature in the field of machine learning and artificial intelligence. The main advantage of this approach is to benefit from a set of classifiers instead of using a single classifier with the aim of improving the prediction performance, such as accuracy. Selecting the base classifiers and the method for combining them are the most challenging issues in the ensemble classifiers. In this paper, we propose a heterogeneous dynamic ensemble classifier (HDEC) which uses multiple classification algorithms. The main advantage of using heterogeneous algorithms is increasing the diversity among the base classifiers as it is a key point for an ensemble system to be successful. In this method, we first train many classifiers with the original data. Then, they are separated based on their strength in recognizing either positive or negative instances. For doing this, we consider the true positive rate and true negative rate, respectively. In the next step, the classifiers are categorized into two groups according to their efficiency in the mentioned measures. Finally, the outputs of the two groups are compared with each other to generate the final prediction. For evaluating the proposed approach, it has been applied to 12 datasets from the UCI and LIBSVM repositories and calculated two popular prediction performance metrics, including accuracy and geometric mean. The experimental results show the superiority of the proposed approach in comparison to other state-of-the-art methods.
近年来，集成分类方法在机器学习和人工智能领域的工业界和文献中都得到了广泛研究。这种方法的主要优点是受益于一组分类器，而不是使用单个分类器，目的是提高预测性能，如准确性。选择基分类器及其组合方法是集成分类器中最具挑战性的问题。在本文中，我们提出了一种使用多种分类算法的异构动态集成分类器（HDEC）。使用异构算法的主要优点是增加基分类器之间的多样性，因为这是集成系统成功的关键。在这种方法中，我们首先使用原始数据训练许多分类器。然后，根据它们识别正例或负例的能力将它们分开。为此，我们分别考虑真阳性率和真阴性率。在下一步中，根据分类器在上述度量中的效率将其分为两组。最后，将两组的输出相互比较以生成最终预测。为了评估所提出的方法，已将其应用于来自UCI和LIBSVM存储库的12个数据集，并计算了两个流行的预测性能指标，包括准确性和几何平均值。实验结果表明，与其他现有方法相比，所提出的方法具有优越性。
