论文标题
简单分类任务是否需要深度学习？
Is deep learning necessary for simple classification tasks?
论文作者
论文摘要
自动化机器学习（AUTOML）和深度学习（DL）是两个尖端范式，用于解决无数的归纳学习任务。尽管取得了成功，但在特定现实世界中的问题背景下，何时选择一种方法而不是另一种方法。此外，相对较少的工具可以在相同的分析中同时整合AutoML和DL，以产生结合其两种优势的结果。在这里，我们试图通过（1.）通过（1.）解决这两个问题，在6个良好的公共数据集对二进制分类的背景下，对AUTOML和DL进行了面对面的比较，以及（2.）评估一种基于基因编程的AutoML的新工具，用于结合深度估计器。我们的观察结果表明，在在类似的数据集上训练以进行二进制分类，但将Automl优于简单的DL分类器，但将DL集成到AutoML中可以进一步提高分类性能。但是，训练Automl+DL管道所需的大量时间可能会超过许多应用程序的性能优势。
Automated machine learning (AutoML) and deep learning (DL) are two cutting-edge paradigms used to solve a myriad of inductive learning tasks. In spite of their successes, little guidance exists for when to choose one approach over the other in the context of specific real-world problems. Furthermore, relatively few tools exist that allow the integration of both AutoML and DL in the same analysis to yield results combining both of their strengths. Here, we seek to address both of these issues, by (1.) providing a head-to-head comparison of AutoML and DL in the context of binary classification on 6 well-characterized public datasets, and (2.) evaluating a new tool for genetic programming-based AutoML that incorporates deep estimators. Our observations suggest that AutoML outperforms simple DL classifiers when trained on similar datasets for binary classification but integrating DL into AutoML improves classification performance even further. However, the substantial time needed to train AutoML+DL pipelines will likely outweigh performance advantages in many applications.
