标题： 从学术大数据中衍生的自动机器学习 显示英文标题

标题： Automatic Machine Learning Derived from Scholarly Big Data

摘要： 应用机器学习的一个挑战性方面是需要确定在给定数据集上表现最佳的算法。 这个过程可能困难、耗时，并且通常需要大量的领域知识。 我们提出了Sommelier，一个用于推荐应在之前未见过的数据集上应用的机器学习算法的专家系统。 Sommelier基于从大量学术出版物语料库中提取的领域知识的词嵌入表示。 当提供一个新的数据集及其问题描述时，Sommelier利用在词嵌入表示上训练的推荐模型，为数据集提供最相关算法的排名列表。 我们通过在121个公开可用的数据集和53个分类算法上进行广泛评估来展示Sommelier的有效性。 Sommelier为每个数据集推荐的顶级算法平均能够达到所有被调查算法最优准确率的97.7%。 显示英文摘要

摘要： One of the challenging aspects of applying machine learning is the need to identify the algorithms that will perform best for a given dataset. This process can be difficult, time consuming and often requires a great deal of domain knowledge. We present Sommelier, an expert system for recommending the machine learning algorithms that should be applied on a previously unseen dataset. Sommelier is based on word embedding representations of the domain knowledge extracted from a large corpus of academic publications. When presented with a new dataset and its problem description, Sommelier leverages a recommendation model trained on the word embedding representation to provide a ranked list of the most relevant algorithms to be used on the dataset. We demonstrate Sommelier's effectiveness by conducting an extensive evaluation on 121 publicly available datasets and 53 classification algorithms. The top algorithms recommended for each dataset by Sommelier were able to achieve on average 97.7% of the optimal accuracy of all surveyed algorithms.