In the present era, enormous factors contribute to causing cancer. So cancer classification cannot rely only on doctor's thoughts. As a result, intelligent algorithms concerning doctor's help are inevitable. Therefore, the authors are motivated to suggest a novel algorithm to classify three cancer datasets; colon, ALL-AML, and leukaemia cancers. Their proposed algorithm is based on the deep neural network and emotional learning process. First of all, by applying the principal component analysis, they had a feature reduction. Then, they used deep neural as a feature extraction. Then, they implemented different classifiers; multi-layer perceptron, support vector machine (SVM), decision tree, and Gaussian mixture model. In the end, because in the real world, especially when working on systems biology, unpredictable events, and uncertainties are undeniable, the robustness of their model against uncertainties is important. So they added Gaussian noise to the input features of the first encoder in each dataset, then, they applied the stacked denoising method. Experimental results disclosed that, generally, using emotional learning increased the accuracy. In addition, the highest accuracy was gained by SVM, 91.66, 92.27, and 96.56% for colon, ALL-AML, and leukaemia, respectively. However, GMM led to the lowest accuracy. The best accuracy gained by GMM was 60%.

中文翻译：

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基于深度神经网络和情感学习方法的癌症分类。

在当今时代，导致癌症的因素很多。所以癌症分类不能只靠医生的想法。因此，有关医生帮助的智能算法是不可避免的。因此，作者有动力提出一种新的算法来对三个癌症数据集进行分类；结肠癌、ALL-AML 和白血病癌症。他们提出的算法基于深度神经网络和情感学习过程。首先，通过应用主成分分析，他们进行了特征缩减。然后，他们使用深度神经作为特征提取。然后，他们实现了不同的分类器；多层感知器、支持向量机 (SVM)、决策树和高斯混合模型。最后，因为在现实世界中，尤其是在研究系统生物学时，不可预测的事件，和不确定性是不可否认的，他们的模型对不确定性的鲁棒性很重要。因此，他们在每个数据集中的第一个编码器的输入特征中添加了高斯噪声，然后，他们应用了堆叠去噪方法。实验结果表明，一般来说，使用情绪学习提高了准确性。此外，SVM 对结肠、ALL-AML 和白血病的准确度最高，分别为 91.66、92.27 和 96.56%。但是，GMM 导致的准确度最低。GMM 获得的最佳准确度为 60%。此外，SVM 对结肠、ALL-AML 和白血病的准确度最高，分别为 91.66、92.27 和 96.56%。但是，GMM 导致的准确度最低。GMM 获得的最佳准确度为 60%。此外，SVM 对结肠、ALL-AML 和白血病的准确度最高，分别为 91.66、92.27 和 96.56%。但是，GMM 导致的准确度最低。GMM 获得的最佳准确度为 60%。