基于人工蜂群算法的核极限学习机参数优化及其在疾病分类中的应用

M. Horng, Jian-Ying Cheng, Yu-Lun Hung, Yu-Cheng Hung, Yung-Nien Sun, Pongpon Nilaphruek
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引用次数: 0

摘要

机器学习方法在疾病分类和诊断中得到了广泛应用,提高了分类和诊断的准确性和效率。核极值学习机算法被越来越多地用于训练单层前向神经网络,该算法给出了输入层和隐藏层之间的权值,以及每个隐藏节点的偏置参数。为了获得更稳定准确的模型,采用人工蜂群算法对核参数和惩罚参数进行预训练。重量和偏差。提出了一种基于人工蜂群的核极限学习机对医疗数据集进行分类。这种方法被称为ABC-KELM。在实验中,我们使用UCI知识库中的乳腺癌和帕金森病两个基准数据集来评估分类的有效性和准确性。实验结果表明,ABC-KELM能获得满意的分类结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parameter Optimization of Kernel Extreme Learning Machine Using Artificial Bee Colony Algorithm and Its Application for Disease Classification
Machine learn methods have been widely used for classification and diagnosis of diseases for increasing its accuracy and efficiency. The kernel extreme learning machine is being increasingly used algorithm to training single layer forward neural network as that this network is given the weights between input and hidden layers, and the bias parameter of each hidden node. In order to obtain more stable and accurate model, an artificial bee colony algorithm is used to pre-train parameters of kernel parameter and penalty parameter. weight and bias. In this paper, an artificial bee colony based kernel extreme learning machine is proposed to classify medical datasets. This proposed method is called ABC-KELM. In experiments, we use two benchmark datasets that are Breast cancer and Parkinson disease from the UCI repository to evaluate the effectiveness and classification accuracy. The experimental results reveal that the ABC-KELM can obtain satisfactory classification results.
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