SaaS software performance issue diagnosis aims to classify the type of the performance records. Deep classification method has gained much attention as a way to construct hierarchical representations from a small amount of labeled data. However, there are few researches on how to solve the classification problem of performance issues by using the deep classification method. In addition, shallow classification methods exist some problems, such as the training sample is large and the ability to fit complex functions is weak. In this article, we proposed a deep performance issue classification method based on Independent Component Analysis (ICA) and Restricted Boltzmann Machine (RBM). ICA is used to extract the features, after this process, the classification feature is obtained as RBM input, and the extracted information about performance issue is transformed into identifiable information for the classifier via visible structure of input; Hidden layer for RBM is built to realize the data transmission between hidden structure, keeping the key information; And the classification algorithm is implemented to solve our performance issue diagnosis problem of SaaS software. Experiments show that the performance of our approach is superior to the classical shallow classification algorithm, and it also meet the efficiency requirement.

中文翻译：

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使用独立组件分析和受限玻尔兹曼机进行 SaaS 软件性能问题诊断

SaaS软件性能问题诊断旨在对性能记录的类型进行分类。深度分类方法作为一种从少量标记数据构建层次表示的方法而备受关注。然而，关于如何利用深度分类方法解决性能问题的分类问题的研究较少。此外，浅层分类方法还存在训练样本大、拟合复杂函数能力弱等问题。在本文中，我们提出了一种基于独立分量分析 (ICA) 和受限玻尔兹曼机 (RBM) 的深度性能问题分类方法。ICA用于提取特征，经过这个过程，得到分类特征作为RBM输入，提取的性能问题信息通过输入的可见结构转化为分类器可识别的信息；为RBM建立隐藏层，实现隐藏结构之间的数据传输，保留关键信息；并实现了分类算法来解决我们SaaS软件的性能问题诊断问题。实验表明，我们的方法的性能优于经典的浅层分类算法，并且也满足了效率要求。并实现了分类算法来解决我们SaaS软件的性能问题诊断问题。实验表明，我们的方法的性能优于经典的浅层分类算法，并且也满足了效率要求。并实现了分类算法来解决我们SaaS软件的性能问题诊断问题。实验表明，我们的方法的性能优于经典的浅层分类算法，并且也满足了效率要求。