Among other machine learning techniques, the extreme learning machine has evidently proved its diagnostic accuracy on many cases in medical domain. Its accuracy mainly depends on the optimal parameters that are used in training. The proposed work is based on optimizing the extreme learning machine using the recently proposed meta-heuristic optimization technique named artificial algae algorithm with multi-light source. In this work, two experiments are conducted using four binary classification datasets related to medical domain. The feasible number of hidden neurons is found from the first experiment using relevant performance parameters. In the second experiment, the classifier with feasible number of hidden neurons is further evaluated with the ten-fold cross-validation method based on its computation time and classification accuracy. In both the experiments, the proposed classifier performance compared with that of other four similar hybrid approaches. It is also statistically compared using Friedman test and Wilcoxon signed rank test based on the area under curve and accuracy values respectively. It is found that the proposed classifier produces better results than the other classifiers.

中文翻译：

---

使用极限学习机和多光源人工藻类算法的混合提高分类精度

在其他机器学习技术中，极限学习机已经在医学领域的许多案例中明显证明了它的诊断准确性。它的准确性主要取决于训练中使用的最佳参数。所提出的工作基于使用最近提出的元启发式优化技术优化极限学习机，该技术名为多光源人工藻类算法。在这项工作中，使用与医学领域相关的四个二进制分类数据集进行了两个实验。隐藏神经元的可行数量是使用相关性能参数从第一个实验中找到的。在第二个实验中，基于其计算时间和分类精度，使用十倍交叉验证方法进一步评估具有可行隐藏神经元数量的分类器。在这两个实验中，所提出的分类器性能与其他四种类似的混合方法相比。还分别使用基于曲线下面积和准确度值的弗里德曼检验和威尔科克森符号秩检验对其进行了统计比较。发现所提出的分类器比其他分类器产生更好的结果。